martin margreiter tum-vt · 11/7/2016 · technische universität münchen - lehrstuhl für...

Technische Universität München - Lehrstuhl für Verkehrstechnik

Univ.-Prof. Dr.-Ing. Fritz Busch

Arcisstraße 21 80333 München, www.vt.bgu.tum.de

MASTER’S THESIS

Mobility Apps – Users’ Needs, Data Requirements

Author:

Gabriel Hernandez Valdivia

Mentoring:

Dr.-Ing. Matthias Spangler (TUM)

M.Sc. Maximilian Schreieck (TUM)

M.Sc. Sabine Krause (TUM)

Date of Submission: 2016-07-10

Technische Universität München - Lehrstuhl für Verkehrstechnik


Arcisstraße 21 80333 München, www.vt.bgu.tum.de

MASTER’S THESIS

of Gabriel Hernandez Valdivia

Date of Issue: 2015-12-15

Date of Submission: 2016-07-10

Topic: Mobility Apps – User Needs’, Data Requirements

Smartphone applications have become one of the main sources of information in the

daily life of many people. Through the so called apps, information from various domains

is transferred to the broad public. Many applications exist for mobility services helping

the user to plan routes, buy tickets, get the traffic state or order a taxi, among others.

The providers of Apps can be diverse, from mobility providers offering Apps for their

provided service, to private companies gathering information from various sources and

providing them in form of an App.

Within this thesis, the various existing services in mobility Apps shall be analyze. A

particular focus within the analysis shall be put on the data sources used to offer the

service. Within a survey, the user needs shall be addressed. Here it shall be found out,

which information is most important to the user and which quality they are expecting

the data to have. Furthermore, it shall be evaluated to what extent the required

information can also be obtained from sources the public authorities collect for the

purpose of traffic management.

The following aspects have to be carried out within the Master’s thesis:

Literature Review on existing mobility apps

Analysis of the data used to provide the service.

Design and conduct of a survey to analyze the user needs.

Evaluation of the survey and analysis of the data and data quality needed to

provide a service, taking in to account the desires of the respondents from the

survey.

Feasibility study for the realization of an appropriate mobility App using data

from public authorities.

The student will present intermediate results:

1. Fifth week

2. Tenth week

3. Fifteenth week

4. Twentieth week

The student will present intermediate results to the mentor(s) (Dr.-Ing. Matthias

Spangler (TUM), M.Sc. Maximilian Schreieck (TUM), M.Sc. Sabine Krause (TUM)) in

the fifth, tenth, 15th and 20th week.

The student must hold a 20-minute presentation with a subsequent discussion at the

most two months after the submission of the thesis. The presentation will be

considered in the final grade in cases where the thesis itself cannot be clearly

evaluated.

____________________________


Abstract

Abstract

This study identify which are the services and data used by smart infrastructure and mobility

Apps. It researches which goals should be addressed by the Apps for mobility. A survey is

applied to identify the satisfaction of the people of the current services and their needs. As a

result, propose of a platform of modular services to address the right goals, needs, preferences

using several data sources and the capabilities of the IT developers.

The market for intelligent mobility is growing intensively. The data collected by Smart

Infrastructure is highly precise but not widely used. The mobility Apps provide a high quality

and deregulated variety of services but struggle to get the data they need. The users expect

an even larger variety of services and better quality. The smart infrastructure is capable to

collect and provide the data the Apps needed. The Apps developers can provide the quality of

services the users want. A Platform offering modular services will convey the capabilities of

both. This will enable the industry to develop solutions closer to both, an individual and a global

optimum.

Contents

Contents

Abstract ................................................................................................................................ 4

Contents ............................................................................................................................... 5

Introduction .......................................................................................................................... 1

1 Methodology ................................................................................................................. 3

2 Literature Review .......................................................................................................... 6

b. Smart Infrastructure ................................................................................................ 9

c. Technologies Based on Smartphones .................................................................. 11

d. Evaluation of Smartphone based Technologies vs Smart Infrastructure .......... 12

e. Societal Implications ............................................................................................. 13

3 Mobility Services and Data ........................................................................................ 16

f. Smart Infrastructure ................................................................................................... 17

3.1.1 Services Provided by Smart Infrastructure ........................................................ 17

3.1.2 Data from Smart Infrastructure .......................................................................... 24

g. Technologies Based on Smartphones .................................................................. 28

3.1.3 Smartphone based Services .............................................................................. 30

3.1.4 Smartphone based Services Data Sources ....................................................... 38

4 Goals of Services for Mobility .................................................................................... 42

h. Goals of Organizations .......................................................................................... 42

i. User Preferences ........................................................................................................ 51

4.1.1 Theory for Statistical Analysis ............................................................................ 52

4.1.2 Survey Design ................................................................................................... 58

4.1.3 Application of Survey......................................................................................... 62

4.1.4 Results of Survey ............................................................................................... 65

5 Services and Data. Smart Infrastructure vs Smartphone Apps ............................... 76

6 Solution: A Platform of Modular Services ................................................................. 81

Conclusions ....................................................................................................................... 89

List of References .............................................................................................................. 92

List of Abbreviations .......................................................................................................... 99

List of Figures .................................................................................................................. 101

Appendix A: App Service Analysis .................................................................................. 103

Contents

Appendix B: Survey Templates ....................................................................................... 105

Appendix C: Survey Website .......................................................................................... 112

Appendix D: Programming Code in R for Linear Regressions ..................................... 115

Appendix E: Results of Linear Regressions ................................................................... 116

a. Service: Journey planner: Reg_1a ....................................................................... 116

b. Service: Navigation: Reg_1b ................................................................................ 116

c. Service: Purchase of tickets: Reg_1c ................................................................. 117

d. Service: Charging stations: Reg_1d .................................................................... 117

e. Service: Parking assistance: Reg_1e .................................................................. 118

f. Service: Vehicle/ride share: Reg_1f ......................................................................... 118

g. Service: Additional information: Reg_1g ............................................................ 119

h. Service: Taxi hailing: Reg_1h ............................................................................... 119

7 Appendix F: Goals addressed by declared service responses .............................. 121

8 Declaration concerning the Master’s Thesis / Bachelor’s Thesis ......................... 123

Introduction

1

Introduction

The contemporary world is rapidly changing; the mega trends of urbanization, climate change,

globalization and digitalization shape the world and our lives today. Near to seven billion people

live on our planet, 54% of them live in cities and this share will rise to 66% by 2050 in a world

of 9.7 billion Humans (United Nations 2014). Urban Mobility becomes a key aspect to ensure

life quality and efficiency of the forthcoming urban agglomerations. The rise in the average

temperatures between 2.2 °C and 8 °C is estimated by then (IPCC 2013). Big challenges are

ahead for all the world and mobility solutions are on the rise joining both, the real and the digital

world. The rise of the market size for Intelligent Mobility is calculated from its current annual

value of € 167 billion to € 1.07 trillion in 2025, six times bigger. This indicates a vast space for

opportunities in this field (Catapult Transport Systems 2015).

Before 1960’s, construction based solutions were the most popular way to manage with traffic

issues within cities and roads. Then in the 1970’s traffic control and traffic management

initiatives were implemented by the cities, called Intelligent Transportation Systems (ITS).

Recently, the automatization of the vehicles is on the rise. However, the improvements of traffic

flow in most cities can reach around 10% of improvement based on current ITS approaches

(Larson and Chin 2016). Therefore, it is needed to apply more integral strategies from different

natures to improve the mobility.

Information Technologies have emerged as new tools to develop innovative mobility services,

providing services closer to the users while facilitating the data collection and analysis. The

age for the Smart City is now, in which cities are connected by means of digital technologies,

their physical and human resources to augment their capabilities and meet stablished goals

(European Laboratory for Urban Innovation 2016). Sadly, the goals are not often stated and

met. A new age of mobility services is happening, a large, diverse and unorganized variety of

them. The specialists have to assure that these are oriented to optimize Mobility concerning to

both a global and individual optimum.

Smartphone applications (Apps) have become one of the main sources of information in the

daily life, nowadays, around 65% of the population in industrialized countries and 45% for

developing countries own a smartphone (PEW Research Center 2015, World Economic Forum

2016). Now, many Apps exist for mobility services. The existing service system categories and

the service modules of the most popular Apps are identified in this research. Then It has been

found that the Apps offer similar services than the ITS but rarely use the data collected with

Smart Infrastructure and prefer to generate and use their own data. The goals of the parties

involved in these services are reviewed, showing that the primary needs are out of their focus.

This leads to the development of a platform for modular services where all the parties can offer

and find modules or data sets to generate more digital solutions for mobility.

This research starts explaining the methodology to be followed which is under the framework

of Design Science Research (Hevner, March et al. 2004). Then reviewing what is being said

Introduction

2

in the literature about smart infrastructure, smartphone Apps for mobility services and social

conditions for both. Afterwards it goes deep in identifying which services offered by the current

ITS and which data they are using. Then the attention is paid to the already existing Apps.

Their services, growth and economics are to be compared. This puts lights onto what kind of

data is used and how often the data generated is used by smart cites. The data that is being

used by both, the smartphones and “in Situ” technologies is to be reviewed in terms of type of

data, collection method, ownership of data and costs.

With a top down approach, reviewing the goals for mobility and the goals for development of

different organizations. Then it identifies the user’s needs for smart mobility services and

understand the goals to follow from the bottom to the top. Then it contrasts both to identify

relevant goals. How these goals are being met by the current services is to be reviewed. The

generation of Apps and digital services in general has been a very dynamic and plural scene.

Therefore, this work proposes how to develop more solutions and reach a bigger success in

the sphere of mobility.

The outline of this work is as follows: In Chapter 1 the methods used to structure the whole

study and each chapter are to be explained. Design Science Research, Qualitative and

Quantitative Literature Reviews, framework for service systems, Descriptive Statistics and

Statistical models. Then, Chapter 2 presents the literature review. Chapter 3 contains the

analysis of services and data used for both Smart Infrastructure and Smartphones. Chapter 4

reviews what goals, organizations related to mobility declare to follow and what the people’s

satisfaction of Apps is. In chapter 5 Both data and services environments, Smart infrastructure

and Smartphones are evaluated. In chapter 6 a solution on how to connect the goals to the

Apps and the Apps to the data is to be presented, with a platform to trade data and digital

mobility services.

Methodology

3

1 Methodology

This research is written following the design science research approach. The task set is to

understand, explain and invent. The focus is also to provide guideline material for further

innovations evading random inventions.

To begin with, the design science approach used differs from the behavioral science. The

behavioral science develops and verifies theories or makes predictions. Its goal is to find the

truth. Design science research goes one step further; it finds the utility of the knowledge in the

real world. It seeks to extend the boundaries of human and organizational capabilities by

creating new and innovative artifacts. The result of the research in design science can be

presented in the form of a process (set of activities) or a product (an artifact), either physical

or digital (Hevner, March et al. 2004).

Peffers, Tuunanen et al. (2007) suggest a six steps procedure to follow design science

research. In the first one, the motivation and the problems are outlined. The second one

defines the objectives of a possible solution. The third step, designs and develops the artifact

or process. In the fourth one, the ability of the artefact to solve the addressed problem is

demonstrated. In the fifth step, the developed artefact is evaluated. Additionally, it is possible

to go back to the second and third steps to improve the artefact. The last step presents the

developed artefact an interested/target audience.

The methodologies used in each chapter are mentioned in their general terms and described

in this section. This work follows the first step, identifying the objectives for a solution in Chapter

2 Literature Review. For the second step, to define the objectives of a possible solution the

Chapters 3 Mobility Services and Data, 4 Goals of Services for Mobility

and 5 Services and Data. Smart Infrastructure vs Smartphone The work goes to the third

step, proposing solutions in Chapters 6 Solution. The evaluation of the artifact and the

presentation of the artifact to the audience, steps five and six, are out of the scope of this work

and can be covered by future research.

A detailed description and how they are applied is presented in each chapter of the thesis, in

this section these are mentioned to work as a roadmap. For Chapter 2 Literature Review, the

method by Webster and Watson (2002) is to be followed. Chapter 3 contains two sections.

The first one dedicated to Smart Infrastructure technologies using the procedure followed by

Leduc (2008) and the second Technologies Based on Smartphones follows the methods to

identify services and categorizes modules proposed by Dörbecker and Böhmann (2015) as

well as by Lacity, Khan et al. (2010).

Chapter 4, reviews different sources of goals for mobility services and summarizes relevant

concepts in to the Maslow’s Pyramid of human needs (Kellingley 2016) to set priorities.

Chapter 5 uses the method presented in the publication of the Leduc (2008) to compare

smartphone based and smart infrastructure services.

Methodology

4

Chapter 3, Literature Review follows the method proposed by Webster and Watson (2002),

popular in the field of Information Sciences. Their method aims to create a bold understanding

of the existing literature in a quantitative and qualitative way. It helps to identify the upsides,

drawbacks and gaps of the current literature in a structured way. It indicates how to select

relevant papers, guides the review in a concept-centric way instead of an author centric one,

and synthetizes quantitative and qualitative results (Webster and Watson 2002).

To identify which papers are to be considered in the review, Webster and Watson (2002) firstly

suggest searching for the keywords in the most significant contemporary journals and

conference proceedings. Then they suggest identifying the most relevant articles and tracking

their citations. From these citations, the papers are taken to a second selection. To organize

the review in a concept-centric way the researcher builds a concept matrix listing the key

concepts of their work (Figure 1). After reading each work, the concepts are identified and

marked in the matrix. The concepts can be grouped or cross-categorized, so different terms or

focuses can be included in broader meanings.

Figure 1: Concept Matrix augmented with units of analysis (Webster and Watson 2002).

In section f the analysis of the services and data sources for Smart Infrastructure is developed

under the structure proposed by Leduc (2008). It identifies the capacities, limitations and costs

as well as its market development.

For section g, three methods are applied to analyze both the Smartphone based services and

the Smartphone based Services Data. Firstly, relevant Apps are identified. Secondly, their

services and modular services, and then they are coded in to existing categories. Finally, the

data sources that Apps use are identified following the same procedure. The key service

modules of service systems for urban transportation are identified, according to (Balzert 2009)

whose work states that module elements are strongly interrelated but only weakly interrelated

with elements outside the module. Then the service’s modules considered are the part of

services that generate a distinct value for the user. For the coding, the iterative coding process

by (Lacity, Khan et al. 2010) is followed and the services’ modules are categorized according

to the existing mobility services framework of Dörbecker and Böhmann (2015). The same

process is followed to identify the data source, checking license files and general terms and

conditions of the Apps.

Methodology

5

Chapter 4 focuses on what the mobility services are addressing. The goals of the Apps are

reviewed in a two sided way, bottom-up and top-down approaches. Firstly, checking what are

the relevant organizations and how they orient their efforts to mobility using a top-down

approach. Then, the user’s preferences are checked by a bottom-up approach. Concepts

taken from both will be summarized as (Kellingley 2016) indicates and the match of both

viewpoints is to be found.

To identify the service satisfaction degree of the users, an online questionnaire of stated

preference of discrete choice is applied. This considers the frequency of usage and the

smartphone Apps knowledge of the users. For the user’s further needs and preferences, the

questionnaire asks for a revealed preference of services. The application followed the

procedure of the evaluation criteria of the Committee on the Use of Humans as Experimental

Subjects (COUHES 2015) of the Massachusetts Institute of Technology (MIT). In which, the

biasing, organizational formalities and data privacy are controlled. The data collected is

analyzed with descriptive statistics and statistical modeling using linear regression for

categorical values (Bruin 2006).

Chapter 5 conveys end evaluates the evidence collected in the previous chapters, similarly as

in the publication of Leduc (2008). The findings of these investigations are conveyed in to

Chapter 6 to the definition of Solution. The methods used assure the scientific approach of this

work.

Literature Review

6

2 Literature Review

This chapter deals with the literature used to develop this work, it dwells on the topics covered

by the sources, provides the results of quantitative and qualitative analysis made on the bases

of articles and papers that convey information about recent progresses in the area of mobility

Apps.

The literature review uses the procedure of Webster and Watson (2002) to identify the open

issues of future research in the fields of mobility Apps. The scope of this research is illustrated

in Figure 2. There are many smartphone applications on the market; this work focuses on

those for mobility services. There are also many Apps for mobility services, i.e. freight

transport, oil prices, float managers. This focuses only on mobility for people, not on their

technical details but rather on their services from the outside, identifying modules of a service

system, coding and comparing them with the services provided by the traditional Intelligent

Transportation Systems (ITS). The data sources are identified for both Apps and ITS services.

The data can be collected with the smartphone, with Smart Infrastructure or with other

technologies such as Floating Car Data (FCD). This research focuses on only smartphone

based vs Smart Infrastructure based services.

Figure 2: Scope of this research marked as the arrows

An all-field-search (title, abstract, keyword, references) was executed with the two queries on

the libraries of the web of science, science direct and google scholar in the fields of

Engineering, Computer Sciences and Environmental Science from 2005 until February 2016.

In the first query” Apps AND (Mobility OR Commuting OR Traffic)” were searched for, resulting

in 27 hits. After reviewing these publications only four were considered relevant, therefore a

second query was necessary. The second query was “apps OR smartphone AND (Mobility OR

Commuting OR Traffic)” resulting in 125 hits; in total from both searches 152 papers were

found. After scanning them, 49 papers were selected. There were 43 scientific papers and six

white papers produced by governments, consultants or think tanks. No additional search for

their references was performed since 49 articles were extensive enough literature search.

Literature Review

7

Another source of literature was that of the mentors of this study, suggested seven additional

papers, adding up to the total of 56 sources. Additionally, other articles were used as sources

to enrich the scientific argumentation. These proved to be relevant for this research in punctual

arguments rather than to orientate the research line. Therefore, these are integrated in the

content rather than in the literature review.

The most relevant literature items were produced after 2008, surely after the invention of the

iPhone and the “app store” by Apple in summer of 2008. Research started in 2005, focusing

on the location awareness estimated with the phone network signals, but it will not be

considered in this study since it was not an App. The number of publications has grown

significantly in the last years. At the beginning of 2016, January and February, almost the same

amount of papers were produced as in the year 2014, as it can be seen in Figure 3. According

to Edmondson and McManus (2007) who evaluate the maturity of research fields, this increase

in the studies of Apps for mobility shows that the topic is rapidly evolving from a nascent to an

intermediate field of research.

Figure 3: Growth of publications in the topics mobility and Smartphone Apps.

The main relevant topics in the scope of this study are:

a. Technologies based on data from infrastructure sensors

b. Technologies based on data from Smartphones

c. An evaluation among the two of them

d. Societal aspects

An article was considered relevant when it covered widely one of these four dimensions and

had a strong connection with another two. 16 works were considered relevant, none of the

papers contained all the concepts searched. From this 16 articles, this study reviewed how

they covered the aspects of kinds of data, data collection, services for users and a market

outlook of technologies based on both sensor’s data and smartphone data. Then for the

evaluation it was reviewed the coverage, quality/accuracy, modularization, costs and the

services they provide. For the social aspects, it was reviewed the social expectations, how the

people reacts to the services, the goals and data ownership. In average the relevant papers

covered the 39% of the topics, the best matches had 59% of correspondence with the target

topics and none of the papers covered all of the studied topics. These numbers show how new

the research in this field is and how spread the literature is. Nearly one third of them spoke

0

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Literature Review

8

mainly about the systems using the data from the infrastructure sensors, half of them described

the smartphone based services, one third discussed a comparison of both systems and less

than the half of them emphasized social aspects (Figure 4).

Figure 4: Topic coverage of the literature review

The least covered topics can be seen as opportunities for future research. In this research,

more comparisons and evaluations between both groups of services based on smartphone

and Infrastructure data, arise as topics with vast opportunities.

Figure 5: Coverage of relevant topics per author

DimensionResearches

analyzingSub-Dimension

Researches

analyzing

1.- Kinds of data 5

2.- Data collection 5

3.- Services for users 5

4.- Market outlook 4

5.- Kinds of data 9

6.- Data collection 7

7.- Services for users 11

8.- Market outlook 7

9.- Coverage 5

10.- Quality/Accuracy 5

11.- Modularization 5

12.- Economics 3

14.- Services 8

15.- People reacting to the services 6

16.- Social expectations 4

17.- Goals 6

18.- Data ownership 11

8

11

Evaluation

Social

Topic coverage by the relevant literature

5

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ipLeduc, 2008 59%Barbaresso et al., 2014 29%Yujuico, 2015 47%Do et al., 2014 24%Lee and Gerla, 2010 53%Herrera et al., 2010 35%Khoo and Asitha, 2016 24%European Committee for

Standarization, 200224%

CATAPULT Transport

Systems, 201559%

Antoniou et al., 2011 59%Zegras et al., 2015 24%Marchetta et al., 2015 41%Sassi et al., 2014 47%Pflügler et al. 2016 24%Krcmar and Schreieck, 2016 47%Baxandhall et al., 2013 29%

Social aspectsEvaluationData from

infrastructure

Data from

smartphones

Literature Review

9

None of the sources covers all of the topics at once and, none of them applies a quantitative

literature review; these facts prove the need of more quantitative approaches for research in

ITS than the traditional scientific methods and the integration of market- and technology

oriented approaches. The content of the reviewed literature was overviewed and recorded in

Figure 4, Figure 5 and Figure 6 and is explicated in the following section.

Figure 6: Coverage of topics per author aggregated

b. Smart Infrastructure

The topic of “Smart Infrastructure” is also mentioned in the literature as Intelligent

Transportation Systems, ITS, Smart Infrastructure, Advanced Traveler Information Systems

(ATIS), Infrastructure Sensors, “In sitú” technologies, and Data for Variable Messages (VMS).

It covers the kind of data, the technology used to collect it and the applications and services

provided to the commuters. Only five of the relevant works reviewed referred directly to it. The

difference in coverage of literature on smartphone data than on “in sitú” technologies can be

caused by the search procedure. The queried keyword was Mobility Apps, and the most of the

Literature Review

10

results were researches in computer sciences and very few in ITS, traditional ITS rarely

focuses on Smartphone apps, arising the question if they should, and if the Apps can become

part of the ITS.

The kind of data collected by means of the infrastructure sensors is discussed in five articles

of the literature review. The study of Leduc (2008) performed a full overview of the different

sensors embedded in the infrastructure by 2008. Different data formats and the ways it is used

and collected is also presented in the work. Lee and Gerla (2010) investigated how the location

of relevant data collected from infrastructure sensors can be shared using the communication

devices on cars. It developed a Vehicular Sensor Network (VSN) Platform to sense events

from infrastructure sensors, save them in the car’s local storage, process them and transmit

them either to other cars or to their infrastructure items. Zegras, K. Butts et al. (2015) identifies

the data relevant for transport and the barriers of its usage in development of new technologies.

Antoniou, Balakrishna et al. (2011) review the capabilities of the data related to transport

collected by sensors and the possible merges of it for improving services. Krcmar and

Schreieck (2016) points out all the Apps in their reviews that use the data collected from

sensors in the infrastructure. The different kinds of data and their capabilities are researched,

but only for a few articles, five out of 16 already selected as relevant works out of 159. More

research is needed for them, within the scope of this research.

The data collection technologies and techniques were mentioned by five of the selected

publications. A full review of the technologies already available was performed by Leduc

(2008), Barbaresso, Cordahi et al. (2014), Antoniou, Balakrishna et al. (2011) and Herrera,

Work et al. (2010), while Lee and Gerla (2010) focused only on Vehicular Sensing Networks,

combining data collected and processed from cars with the data collected by embedded

sensors. Similarly, with the previous sub-dimension, from 159 articles filtered in to 16, only five

of them in this topic, denote a separation between Apps research and ITS.

Only five of the authors reviewed the applications and Services created with the sensors

embedded in the infrastructure. The Leduc (2008) present a state of the art of what was being

provided in 2008 while the US ITS Plan by Barbaresso, Cordahi et al. (2014) suggest what the

future of ITS will look like for the next five years in the USA. Yujuico (2015) did a historical

overview of the traffic management initiatives in Manila, such as Radio broadcasting and

Variable Message Signs and compared them with a new App they developed. Lee and Gerla

(2010) reviewed trends of vehicular sensing applications of traffic flow estimation, urban

surveillance, vehicular safety warning services, road quality monitoring and location aware

micro blogging (threads as comments in websites). European Committee for Standarization

(2002) focuses on establishing various goals and objectives for mobility services as such

considering both of the data sources. Antoniou, Balakrishna et al. (2011) reviews the

technologies that can be provided for traffic management and public transport travelers and

classify them according to the type of data used. This research uses this knowledge to build

upon it.

Literature Review

11

An important aspect of the technology life is the industry and market outlook, but only four of

the authors drew their attention to it, 25 of the total reviewed. Leduc (2008) analyzes the

development of the market of such technologies and estimates how they will be influenced by

the emerging of technologies for smartphones. Barbaresso, Cordahi et al. (2014) also worked

on the trends in research and development of ITS to switch the orientation of the Government

of the United States into the implementation of ITS. Antoniou, Balakrishna et al. (2011) even

evaluates the level of maturity of the programs. The low coverage of this field might the

technical background of the authors or novelty of the area in technology. Therefore, this

research will also focus on the attempts to connect industry and market concepts.

c. Technologies Based on Smartphones

11 of the publications observed technologies based on the data from smartphones considered

the services provided with data from the smartphones and they also mentioned in the literature

as Floating Phone Data, Floating Cellular Data, or even sometimes Floating Car Data, outlining

that it comes from smartphones.

Nine of the publications addressed the kind of data collected by smartphones. Leduc (2008)

reviews the kind of data a smartphone can collect and its applications. Lee and Gerla (2010)

researches how to collect and share data for specific applications as traffic flow prediction,

urban surveillance, warning services, road quality monitoring and location aware micro-

blogging. (Sassi, Marco Mamei et al. 2014) Traces the path from smart mobility services to the

data needed indicating the different types of them. Zegras, K. Butts et al. (2015) makes a full

review of the data needed for transportation purposes which is collected by smartphones.

Antoniou, Balakrishna et al. (2011) addresses mainly traffic data collected by any mean,

smartphones included, while Zegras, K. Butts et al. (2015) are more innovative creating an

application to collect data of different natures by means of questionnaires spatio-temporally

referenced. Marchetta, Natale et al. (2015) propose an architectural design able to collect,

update and process heterogeneous data from sources as smartphones or probe vehicles to

measure different actors of the public space like public and private vehicles, pedestrians and

infrastructure. Krcmar and Schreieck (2016) investigates the variety of data sources needed

for smartphone based services and their origin.

Smartphones are only one device full of sensors, so they can collect a wide variety of data.

Besides, a full arrange of collection techniques increase the kind of data they can collect,

summing up to a wide variety of variables to measure. Therefore, they are essential for this

study. The technologies and techniques of data collection have been addressed in the

literature, seven of the reviewed researches spoke about them. Leduc (2008) and Antoniou,

Balakrishna et al. (2011) consider smartphones as a simply another kind of sensor, which can

collect a wider variety of data and also reviews the different technologies to transmit them as

CDMA, GSM, UMTS and GPRS. The report Zegras, K. Butts et al. (2015) names five primary

mechanisms for data creation with smartphones: manual collection, open and closed crowd-

sourcing, sensor derived and generated by a service provider, reviewing them. Many of the

authors concentrated on spatio-temporal information only and traffic-related issues like

Literature Review

12

Herrera, Work et al. (2010) and Lee and Gerla (2010). The research of Marchetta, Natale et

al. (2015) is wider, incorporating data infrastructure, pedestrian and public services, when

Zegras, K. Butts et al. (2015) evaluates a new data collection technique called “Flock sourcing”,

in which, not a crowd but a trained staff has collected specialized data. Data collection

techniques are beneficial for the smartphone and will be taken into consideration as a provided

service.

The data takes a long way from the collection to the customer usage as a service, about two

thirds of the reviewed literature focuses on this path as well. The other focus only on one part

of it, either on the data or on the data analysis or on the services. In the research of Yujuico

(2015) an App for the public transport of Manila was developed. Do, Dousseb et al. (2014)

developed a location predictor for the users; Lee and Gerla (2010) also describe traffic

services. In their traffic application, Khoo and Asitha (2016) analyze how the users perceive

the current traffic and which data is needed for that. Antoniou, Balakrishna et al. (2011) look

for the generation of more traffic services from different kinds of data and collection techniques

that can possibly be used. European Committee for Standarization (2002) controls the

provision of these services with regulations. Zegras, K. Butts et al. (2015) and Krcmar and

Schreieck (2016) review which are the existing services provided through smartphone Apps.

Marchetta, Natale et al. (2015) provide different transport services through a Map while

Baxandhall, Dutzik et al. (2013) and Krcmar and Schreieck (2016) look for the future of the

services provided, proposing new options and digital architectures.

Smartphone based technologies might play a very significant role in the current industry and

market of mobility, nevertheless, only seven, less than the half of the authors paid attention to

this situation. Leduc (2008), Zegras, K. Butts et al. (2015), Baxandhall, Dutzik et al. (2013),

Krcmar and Schreieck (2016) and Antoniou, Balakrishna et al. (2011) reviewed the current role

and the future of these technologies, market players and barriers for development. Barbaresso,

Cordahi et al. (2014) declare how they will be fostered and incorporated in governmental plans,

when Yujuico (2015) has analyzed the market conditions of the traffic App they develop.

d. Evaluation of Smartphone based Technologies vs Smart

Infrastructure

A comparison between the technologies based on Smartphone and the technologies based

on in sitú sensors was performed by 34 % of the references in terms of coverage of sensors

needed, costs, quality and accuracy of the information, the capabilities to provide services as

well as their modularization and public reaction to these services.

The most of the literature shows awareness of the popularity of the smartphones or the sensors

in the infrastructure, nevertheless only nine authors, roughly the half, focus on a comparison

of the amount of sensors or smartphone needed to provide a service. Yujuico (2015), Do,

Dousseb et al. (2014) and Lee and Gerla (2010) focused only on the coverage needed to

provide the information of their initiative, and they agree that it is cheaper to use smartphones

Literature Review

13

and they need less devices than the infrastructure sensors. Antoniou, Balakrishna et al. (2011)

make a wider review, comparing the sensors necessary for traffic information with the

smartphones needed for those aims and propose data merging methods.

The coverage is useful only if it is equal in the quality of the service through the accuracy of

the data but even less authors mentioned it, five in total. A wider perspective was viewed by

Antoniou, Balakrishna et al. (2011), Lee and Gerla (2010), Marchetta, Natale et al. (2015) and

Herrera, Work et al. (2010) who also evaluated the quality of the services they developed.

To offer a service is good, to offer many services is even better, but to provide full services

and partial services is much better, therefore the modularization of the services is a valuable

strategy. From the selected literature only five articles reviewed its modularization. This is a

new trend in digital services and therefore only the newest researches addressed it. They

identified the main service and their service modules provided via smartphone Apps.

Marchetta, Natale et al. (2015), Sassi, Marco Mamei et al. (2014) to develop their own service,

a mobility map and a rideshare system; Pflügler, Schreieck et al. (2016) proposed a modular

platform of data and services and Krcmar and Schreieck (2016) identified existing modular

services in the App world.

The economic aspects of the technologies will keep it alive in the market and develop further

but only three of the selected works compared the costs of the technologies based on

smartphones and the traditional methods. Only Herrera, Work et al. (2010), Antoniou,

Balakrishna et al. (2011) and Leduc (2008) provided a comprehensive check. They agree with

the argument that smart infrastructure is very expensive and that this data can be collected via

smartphones. Nevertheless, the infrastructure was already acquired in many cities and should

be used.

The services provided by technologies based smartphones or by traditional technologies were

compared by eight selected articles. The researches who developed technology, Yujuico

(2015), Lee and Gerla (2010), Khoo and Asitha (2016) and Marchetta, Natale et al. (2015)

compared their smartphone based services with the analogue ITS one. And only the newest

wide reviews by Pflügler, Schreieck et al. (2016) and Krcmar and Schreieck (2016) looked at

both, services provided using smartphone data and using data collected by sensors and

evaluating them. Baxandhall, Dutzik et al. (2013) compares both and assess their effects on

the behavior of the population in the USA while driving.

e. Societal Implications

The social implications reviewed in this work is the reaction of people to the services, their

expectations, if the work addressed specific goals related to social benefit and owners of the

data. Only 11 out of 16 authors addressed topics of: People reacting to the service, social

expectations, Goals and Data Ownership.

Literature Review

14

Although this topic is strongly related to the mobility of people, only six articles of the literature

depth in the people’s reactions to the services. Khoo and Asitha (2016) applied a questionnaire

and checked the people’s preferences for services on their traffic App, Yujuico (2015) as well

as Sassi, Marco Mamei et al. (2014) monitored the implementation and diffusion of his App to

evaluate its acceptance to show its benefits over traditional technologies. Krcmar and

Schreieck (2016) and Baxandhall, Dutzik et al. (2013) focused on the most popular Apps to

analyze them, considering frequency of usage.

The social expectations, what people wait from the services, were the least covered topic, only

four out of all the authors wrote about them. Khoo and Asitha (2016) asked the public which

features they would prefer for a new driving navigator. Sassi, Marco Mamei et al. (2014) also

addresses the individual mobility needs with urban-scale mobility issues. European Committee

for Standarization (2002) encourages the transport planners to create indicators addressed to

the people’s preferences. Zegras, K. Butts et al. (2015) made a research on expectations as

well, but only regarding the security. Therefore, this literature will reach the customers and

inquire their demands.

The discussion at the highest level is on which goals to address. It enables the research to

develop the solutions that are more robust. It should be presented in every research but it has

been presented only in five of them. The (European Committee for Standarization 2002) is a

guideline that explains how to measure the quality of a transport network, stablishing a goal,

target, indicator framework. The researches based on locations like the Barbaresso, Cordahi

et al. (2014) and Baxandhall, Dutzik et al. (2013) reviewed the technologies needed for the

development of Digital Mobility services in the USA. Yujuico (2015) developed his solution

towards economy, effectiveness, efficiency and equity. Zegras, K. Butts et al. (2015) also

reviews the questions of what the technologies are solving, and what they should be solving.

Sassi, Marco Mamei et al. (2014) understands mobility as a socio-technical system and orients

the solution of the research towards specific goals.

Within the social aspects, the possession of the data collection is an

aspect related to governance and privacy rights, therefore, how the

data will be owned is a critical aspect. 11 articles out of the all literature

overviewed addressed this problem. The wide reviews of Leduc

(2008), Barbaresso, Cordahi et al. (2014), Zegras, K. Butts et al.

(2015) and Krcmar and Schreieck (2016) discuss how the data is

stored, shared and governed. Pflügler, Schreieck et al. (2016)

indicates how to store and regulate the access to the data their

platform offers. Zegras, K. Butts et al. (2015), Marchetta, Natale et al.

(2015) Sassi, Marco Mamei et al. (2014), Herrera, Work et al. (2010),

Do, Dousseb et al. (2014) and Yujuico (2015) reported where their

technology stores the data and under which legal terms.

Figure 7: Boxplot measuring how many articles (16) address the target topics

Literature Review

15

The 18 different topics were addressed by few authors, the half of the research covered

between five and 7 topics. As it can be seen in Figure 7, the most complete works covered 11

topics and the least complete covered only three of them. This shows still a low coverage of

the selected topics in the literature and they are mostly split and heterogeneous, indicating the

opportunities for this research to provide new knowledge.

The literature documented has proven to be vast and highly specialized that it has lost its

connection from other natures of knowledge. The pursue of improvement has often overseen

other discussions like if their approach is the best way to pursue goals of mobility or what does

its customers think of it. It has focused on the improvement of the technology, losing its goals.

Besides, the limits between services using the data from sensors and smartphone data are

blurry and unclear. Therefore, this study reviews the connection of Information Technologies

with ITS and Mobility Management. This research will explain and understand what is the

environment where mobility Apps are developed.

Mobility Services and Data

16

3 Mobility Services and Data

The digitalization trend has strongly influenced the human mobility. Technology has always

been oriented to improve mobility; at the beginning with mechanic machines then with

construction solutions, after that electronic devices were applied. Now is the century of the

digital revolution and this Chapter explore these changes. At the beginning, the relevant terms

are introduced before presenting smart infrastructure and Smartphone based services.

Products are solutions where the user and provider have only a one-time interaction, that can

be either an object or an action. Services encompass a set of interactions within users and

providers. For the case of digital mobility services, the services enable a person to get from A

to B most efficiently.

Services and service systems have emerged as key concepts as technology enables service

systems in different industries as transportation, manufacturing and healthcare i.e. Service

systems enable co-creation of value and therefore leverage the capability the transportation

systems and their different applications. Böhmann, Leimeister et al. (2014) call for future

research on service systems engineering, mentioning mobility as one promising area where

services can generate significant benefits.

Before 1970’s, construction based solutions like overpasses, bridges and new roads, were the

most popular way to manage with traffic issues within cities and roads. Then in the 1970’s ITS

started to be implemented by city administrators to improve safety, efficiency and convenience

of surface transportation. ITS monitor and analyze traffic data and influence the traffic flow

using a variety of measures such as dynamic traffic signs, lights and automatized vehicle

moves. ITS have a positive impact on energy, safety and environmental benefits as well as

construction based measures. However, the improvements of traffic flow in most cities can be

only around 10% based on current ITS approaches such as sensing the road network,

predicting the demand and controlling traffic signaling (Larson and Chin 2016), therefore it is

needed to apply different strategies from different natures to improve the mobility in cities.

Information Technologies have emerged as new tools to influence traffic and improve mobility

(Wolter 2012, Khoo and Asitha 2016). An App is a piece of software that can be run on the

internet, on a computer, on a smartphone or on another electronic device, basically, it is a short

computer program (Karch 2016). ITS and IT are therefore suitable to optimize the Mobility in

cities with regards to both a global and individual optimum.


17

f. Smart Infrastructure

3.1.1 Services Provided by Smart Infrastructure

The interest on improving the traffic situation has been growing and the technology to improve

traffic has significantly evolved. Traffic data collection devices improved astonishingly so as

the techniques to collect the data. And after the data is collected and analyzed, what happens

next? How are the users reached? The services provided are explained in the following

section.

The “in situ” technologies are also called “smart infrastructure”, “Advanced traffic management

systems (ATMS)”, “Advanced traveler information systems (ATIS)” and “Advanced Public

Transportation Systems (APTS)”. They are a part of the ITS that are strictly embedded in the

infrastructure to provide a wide variety of services. A full presentation of them is shown in

(Department of Transportation 2009), which organizes the ITS services in the following

categories: Arterial Management, Transit Management, Freeway Management, Traffic

Incident Management, Information Management, Crash Prevention and Safety, Emergency

Management, Commercial Vehicle Operations, Road Weather Management, Electronic

Payment & Pricing, Intermodal Freight, Roadway Operations and Maintenance and Traveler

Information (Figure 8).

Figure 8: Applications overview website. (Department of Transportation 2009)

For the scope of this work only the intelligent Infrastructure methods will be taken in account,

not considering intelligent vehicles or ITS for public transport which are not dealing with the


18

infrastructure. The services are enlisted according to the categories as shown in Figure

9.These services require different data sources, for some of them they combine different data

sets. Others analyze the data in different ways to provide different services for instance, the

variable speed limits service use the data to monitor the traffic flow, density and speed. These

measurements are taken and considering the conditions of the road and weather data, a speed

limit can be calculated and set. These services are applied in the VAMOS project in Dresden

and will be explained in the next section.

Figure 9: Services provided by ITS. Adapted from: (Department of Transportation 2009)

Video

SurveillanceTraffic Control

Lane

management

Parking

Management

Information

DisseminationEnforcement

TrafficAdaptive Signal

ControlHOV Facilities Data Collection

Dynamic Message

Signs (DMS)

Speed

Enforcement

Infrastructure Advanced Signal

Systems

Reversible Flow

Lanes

Information

Dissemination

In-vehicle Systems

(IVS)

Traffic Signal

Enforcement

Variable Speed

LimitsPricing

Highway Advisory

Radio (HAR)

Bicycle and

pedestrianLane Control

Special EventsVariable Speed

Limits

Emergency

Evacuation

Operations

and Fleet

Management

Information

Dissemination

Transport

Demand

Management

Safe &

Security

AVL/CAD In-vehicle SystemsRide-share

Matching

In-vehicle

surveillance

Transit Signal

Priority

In Terminal /

Wayside

Dynamic Routing

scheduling

Facility

Surveillance

PlanningInternet /

Wireless/Phone

Employee

Credentialing

Service

Coordination

Remote Disabling

Systems

Video

SurveillanceRamp Control

Lane

Management

Special Events

Transport

Management

Enforcement

Traffic Ramp Metering

HOV (High

Occupation

Vehicles) Facilities

Occasional EventsSpeed

Enforcement

Infrastructure Ramp ClosureReversible Flow

LanesFrequency Events HOV Facilities

Priority access Pricing Other EventsRamp Meter

enforcement

Lane Control

Temporary Traffic

Management

Center

Variable Speed

Limits

Emergency

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Variable Speed

LimitsPricing

Highway Advisory

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Bicycle and



Limits

Emergency

Evacuation

Operations

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Management

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Transport

Demand

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In-vehicle

surveillance

Transit Signal

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Wireless/Phone

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In-vehicle Systems

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Traffic Signal

Enforcement

Variable Speed

LimitsPricing

Highway Advisory

Radio (HAR)

Bicycle and



Limits

Emergency

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Operations

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19

Figure 9 (Continues): Taxonomy for classification of services provided by ITS for intelligent infrastructure.

Hazardous Material

Management

Emergency Medical

ServicesResponse & Recovery

TrackingAdvanced Automated Collision

Notification (ACN)

Early warning system for big scale

disasters

Detection Ambulances with TelemedicineResponse Management (Tracking of

emergency fleets)

Driver Authentication Emergency Vehicle Traffic light

preference

Route PlanningEvacuation and Re-Entry

Management

Emergency traveler information

Credentials administration Safety assurance Electronic screeningCarrier Operations & Fleet

ManagementElectronic funds Safety information exchange Safety screening Automatic Vehicle Location

Electronic registration / Permitting Automated inspection Clearance at national borders On-board monitoring

Weight Screening Traveler Information

Credential checking

Surveillance, Monitoring

and prediction

Information Dissemination

for AdvisorsTraffic Control Strategies

Response and Treatment

Strategies

Pavement Conditions Dynamic Message Signs (DMS) Variable Speed Limits Fixed Winter Maintenance

Atmospheric conditions Internet / Wireless / Phone traffic Signal Control Mobile Winter Maintenance

Water Level Highway Advisory Radio (HAR) Lane Use / Road Closure

Vehicle Restrictions

Toll collection

Transit fare payment

Multi use payment

Pricing

Freight tracking Asset tracking

Freight terminal processes

Drayage Operations

Freight-Highway connector system

International border Crossing

Process


for AdvisorsAsset Management Work Zone Management

Dynamic Message Signs (DMS) Fleet Management Temporary Traffic Management

Internet / Wireless / Phone Infrastructure Management Temporary Incident Management

Highway Advisory Radio (HAR) Lane Control

Variable Speed Limits

Speed Enforcement

Intrusion Detection

Road Closure Management

Pre-trip and end-route

informationTourism & events

Internet/Wireless Travel services

Information for traveling Advanced parking

Phone services

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Strategies





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Drayage Operations



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20

These ITS services are used in many cities. One of them is Dresden, in Germany. The City of

Dresden developed an advanced traffic management system and implemented it in the

framework of the project: “Traffic Management System for Dresden” –

Verkehrsmanagementsystem für Dresden (VAMOS Projekt 2012). The services implemented

are: dynamic parking systems, dynamic signaling in highways, radio reports of traffic situation,

dynamic routing with signals, traffic information systems and green waves depending on the

traffic situation.

Dynamic Parking System

The dynamic parking system provides information about the capacity and occupancy of

parking garages, facilities (buildings) and parking squares/zones in the Dresden Downtown

and Park and Ride (P+R) Stations. This initiative aims to decrease the traffic when searching

for a parking spot, since the system can deliver information of the parking options nearest to

the destinations. According to Giuffrè, Siniscalchi et al. (2012) the seeking for parking space

is responsible for up to 40% of the total traffic. Parking seekers often drive slowly and cause

the traffic flow behind them to slow down (Inci 2014). The system collects the information at

the facility with sensors and provides it through dynamic signals. It also cooperates with the

dynamic routing, explained in the following section (Figure 10).

Many solutions like these ones exist on the market (General Electric 2015, Siemens Mobility

2015, SmartParking 2015). In the scientific literature, they are overviewed by Sujith, Yacine et

al. (2014), McNeal (2013) and Seong-Eun, Poh Kit et al. (2008). The advantage of this

infrastructure based parking systems is that the parking information is very precise (Nawaz,

Efstratiou et al. 2013). The main disadvantage is their expensive costs, what is demonstrated

by Nandugudi, Ki et al. (2014): the equipment necessary for one single parking lot costs around

US$ 2500. There are also additional costs for cabling, interfaces and communication systems.


21

Figure 10: Parking information and routing display on a dynamic signal. Taken from (VAMOS Projekt 2012)

The system collects the information with either

vehicle barriers or with induction loops, the

vehicles entering and leaving the facilities are

identified and a digital signal is sent to the

system to provide information of the availability

of parking spaces. The information is mainly

provided with dynamic traffic lights deployed at

intersections and on streets in the route to the

targeted facility. The closer to the parking

facility, the more accurate the information gets.

At the same time, the system distributes the

demand of the parking seekers in to the

different parking facilities, in order to avoid the

traffic generated by cars waiting for other cars

to park. (VAMOS Projekt 2012) This is a useful

initiative however; it is unclear because the

system cannot identify the destination of the

people to provide accurate information for their

parking.

Dynamic Signals on Highways to Influence the Traffic Flow

The dynamic signals aim to influence the drivers to ensure the traffic safety and optimize the

traffic flow. The impacts of accidents, construction sites, irregularities and weather conditions

in the traffic can be minimized. These conditions are monitored and combined with the

information of the traffic flow, to influence the traffic flow positively. The traffic flow is influenced

by means of the dynamic signals showing information about the weather conditions, directions,

irregularities on the road, driving behavior and speed limits (

Figure 11). Moreover, the system can calculate the speed of the traffic flow, identify the best

speed for all the motorists and establish the maximal speed to benefit all the motorists in that

section.

Signaling systems are being widely used in Germany and gained popularity in developed

countries from the years 2000 – 2010, however, these dynamic signals require also expensive

costs of implementation, operation and maintenance for the investors in infrastructure (often

governmental administrations) but none for the user.


22

Figure 11: Dynamic signals to influence the traffic flow used in VAMOS Dresden (VAMOS Projekt 2012)

The information of the urban traffic state in the local radio stations

The traffic information streamed through the local radio stations influences the traffic behavior

with information of the traffic irregularities and risks. Furthermore, the current technology can

connect to the GPS device of every car and reroute the journey or show notifications in real-

time. It can transmit the cause, magnitudes and dimension of the traffic incident. The VAMOS

project reported that this service is only available for non-urban roads due to technological

reasons.

The data feeder for this service is a group of sensors built aside the road and the inputs of the

police crew on those road sections. Currently, the sensors can be installed only within sections

length from 25 km to 100km. This service includes a notifications generator which works 24

hours a day. The notifications generator reads the data and translates it in to a notification for

users and for the local radio stations.

The dynamic routing in the metropolitan area of Dresden allows drivers to calculate their trips

according to the current traffic conditions. Its display consists of a static frame with plates called

“Prismenwendern” that rotate to show different messages.

One disadvantage is that signals can show only a few predefined messages, and they are

unprepared for other unexpected conditions. Another challenge is that the drivers who already

passed the signal will not be able to see the message and their situation will not be improved.

The data used is mostly collected at the highways; the city of Dresden is also covered with 50

signals at 22 intersections. The data flows from its detection at the streets through the data

analysis based on filters and criteria, to the delivery to the user by means of the signal. The

critical street-sections are already identified and their alternative route is already known and

prepared in the movable pieces of dynamic signal.

Speed

Driving behavior

Irregularities

Directions

Weather conditions


23

Figure 12: Dynamic signal - Prismenwendern changing from normal conditions to traffic jam. Taken from (VAMOS Projekt 2012)

Traffic information system

The traffic information system is an addition to the signaling system previously presented. It

can deliver information about traffic jams in different streets, saturated parking areas, train

arrivals and departures etc. These signals can be adapted to show different messages, as

urgent urban or traffic issues, high air pollution levels or demonstrations happening on the

roads i.e.

Figure 13: Display of traffic information. Taken from (VAMOS Projekt 2012)

Adaptive traffic light programming:

The green waves proved improvements on the traffic flow, nevertheless the traffic conditions

are far from being regular and predictable, therefore adaptive traffic lights are a bolder solution.

The green waves can be manually activated to set in which direction the traffic will be eased

or the system can automatically detect and solve it. It can also be adapted, applying partial

green waves to priority sections.

There are also services provided not only to the end users, but to the administration of the

infrastructure. These are the measurements using the infrastructure that allow the authorities

to plan the new infrastructure. These are noise levels, air pollution, traffic flow, Occupation

rate, Vehicles categories, Speed, Travel time, Origin-Destination information and Incident

detection among others. More complex services are the estimations of the Average Annual

Daily Traffic (AADT) and the Vehicle Kilometers Travelled (VKT).


24

These traffic variables assist the traffic engineering analysis for Model calibration,

determination of traffic exposer functions, infrastructure design, policy decisions, risks of

accidents, i.e.

The first one, The Average Annual Daily Traffic (AADT) is the average calculated over a year

of the amount of vehicles passing a point in a given section every day. It is usually expressed

in vehicles per day. This measurement can be taken straight forward from the road. The

guidelines to take these measurements can be found in the publication of Ehlert, Bell et al.

(2006).

The second one, the Vehicle Kilometers Travelled (VKT) refers to the distance travelled by

vehicles on the roads. It is an indicator of traffic demand and generally is used to indicate

mobility patterns and travel trends. VKT can be presented as a value to reflect community

behavior or can be broken down in VKT per capita to report about individual contributions. This

value is harder to measure. Several methods can be used to estimate it. Four methods are

widely applied in Europe: Odometer readings (vehicle –based method), Traffic counts (road-

based method), Driver survey (people-based method) and an estimation based on fuel

consumption. Only one of them is relevant for this study, the Traffic counts (road-based

method). This method uses the previously explained AADT and multiplies it by the length of a

link (in km) which is in focus. A full review on its calculation is presented in the work of Fricker

and Kumapley (2002).

Once that it is clear, which services are provided by Smart Infrastructure, then, what is the data

they are using? These services require different data sources, some of them use only one.

Some others combine different data sets and others analyze the data in different ways to

provide different services. The data they collect and use will be reviewed in the next section.

3.1.2 Data from Smart Infrastructure

The data is the main source for ITS services. This section reviews what data is used to provide

the services and how is this data collected. It includes also descriptions of devices, formats

and examples that currently exist. In the last years, such alternative data sources have

emerged as vehicle location (Floating Car Data) and Vehicle to infrastructure (V2X), which are

out of the scope of this work.

The work of Leduc (2008) and Antoniou, Balakrishna et al. (2011) illustrate the kinds of

technologies in this field, separating them into two categories intrusive and Non-intrusive,

depending on whether the measures were directly taken or not. The intrusive method as the

matter of fact is fulfilled by a data recorder and a sensor placed on or in the road. They are the

most commonly used and the most important ones. This equipment can be briefly described

as following:

Pneumatic road tubes: Rubber tubes placed across the road lanes to detect when a vehicle

passes over the tube. The pressure changes by the weight of the vehicle, creating a pulse of


25

air which is processed by a counter located on the side of the road. This technology can provide

various information such as the number of vehicle, speeds, weight etc. however, the main

drawback of the technology is that it is limited to one lane and its efficiency is highly influenced

by the weather, temperature and traffic conditions. The system is not efficient for low speed

flows.

Piezoelectric sensors: The sensors are placed in a line along the road surface of the lane

monitored. The prefix piezo- is Greek and stands for 'press' or 'squeeze'. These sensors

convert the mechanical energy into electrical energy. The mechanical deformation of the

piezoelectric material generates a difference in the electric potential between the electrodes.

The amplitude and frequency of this signal is proportional to the degree of deformation and it

is recorded as the measurement. These sensors can measure weight, speed and traffic flow

and are more reliable than pneumatic road tubes.

Magnetic loops: The most conventional technology to collect traffic data is the use of

magnetic loops. They are coiled wires into loops and embedded in roadways in a square

formation that generates a magnetic field. When a vehicle (as a big metallic body) passes over

them, it changes the magnetic field, generating a pulse of electricity. This pulse is transmitted

as information to a counting device placed on the side of the road. This technology has been

widely deployed in Europe in the last years; nevertheless, it has proved to have a short life

expectancy, since it can be easily damaged by heavy vehicles. Its implementation and

maintenance costs are expensive but it is not affected by bad weather conditions.

Non-intrusive techniques are based on a remote observation that does not have direct contact

with the vehicle. They have recently emerged with using different kinds of technologies and

the digital revolution is boosting them. The most important ones are the following:

Manual counts: It is the most popular method since it involves only people, pens and papers.

Observers are trained to gather traffic data that cannot be efficiently obtained using technology

e.g. pedestrian counts, vehicle classifications, irregular behavior at intersections and vehicle

occupancy. The most common equipment necessary is the tally sheet, mechanical count

boards, and electronic count board systems.

Passive and active infrared: The presence, speed and type of vehicles can be detected by

the infrared energy reflecting after an infrared beam is projected to the detection area. This

method is heavily affected by the weather conditions and has a limited lane coverage.

Passive magnetic: Magnetic sensors are built in the pavement. They can count the amount

of vehicles passing, their type and speed. In high traffic flows they might fail differentiating

measurements of vehicles passing close by.

Microwave radar: The microwave radars are devices throwing microwave beams to the roads

and detecting moving vehicles. It can record count data, speed and simple vehicle


26

classification. Speed can be recorded us4ing a second radar and measuring the (Doppler

effect).

Ultrasonic and passive acoustic: These devices throw a wave of sound and record the signal

returning to the device. Basically, they measure the time the signal takes to return. Their

frequency is out of the human hear but they can be easily affected by weather conditions. The

ultrasonic sensor is placed alongside the road and can record vehicle counts, speed and

classification data.

Video image detection: These devices are video cameras capable to identify image patterns

and characteristics through Optical Character Recognition (OCR) Software. They can identify

plate numbers, trip lane, vehicle occupation and tracking i.e. These devices have expensive

costs and are sensitive to weather conditions.

The kind of data these sensors collect is presented in Figure 14 and compared according to

its capabilities. The most of them are focused on counting vehicles. The second most popular

categories are to measure the speed, the type of vehicle and its occupancy. Some of them can

detect an accident and very few can provide travel times or Origin/Destination information,

which is the most relevant for transport planning. These comparisons are based on the

researches of Martin, Feng et al. (2003), Schmidt, Giorgi et al. (2005) and the U.S. Department

of Transportation (2006).

Figure 14: Type of data provided by different data collection technologies. Adapted from (Schmidt et al., 2005) and (U.S. Department of Transportation, 2006) and (Peter Martin, 2003).

How precisely does the data look like? The data collected for the (VAMOS Projekt 2012) is an

illustrative example. While looking at it, it the degree of complexity of raw data can be better

understood, it is highly complex. At the course “Mobility Services Lab” the programmers got

this data to produce visualizations and explore the possibility to generate a service with it. They

Volume / Count SpeedVehicle

categoryOccupancy Travel time

O/D

information

Incident

detection

Doppler

True Presence

I

n

t

r

u

s

i

v

e

Video Image processing with

ANPR

Radar

Ultrasonic

Detector type

Inductive Loop

Passive Infrared

Pneumatic Road Tube

N

o

n

I

n

t

r

u

s

i

v

e

Active Infrared

Microwave

Radar

Passive acoustic

Video Image Processing

Manual Counts

Piezoelectric cable


27

have found it very hard to understand, manage and build in a solution from the provided data.

In conclusion the data was not used and they preferred to generate the data by their own with

the smartphones. A similar situation happened during the BMW Automotive Hackdays at the

Technische Universität München in March 2016. Out of the ten proposals developed by the

teams, all of them were defined by the available data. An example of the data collected by

different sensors can be seen in the following Figure 15:

Sensor type Kind of data / German

Kind of data Exemplary value

All of them Information Sensor ID, Camera ID, Lane, Position, Port, IP, Direction, Sensor ID code, Timestamp, Kind of

parking, Description, Capacity, Opening hours, prices

Double induction loop

Geschwindigkeit Speed 23

Belegung Occupancy 22

Belegung_SV Occupancy freight 0

Traffic camera (KPD) LOS (1 - 6) LOS 1

Traffic Observer Laser Measuring System (LMS)

LOS (1 - 6) LOS 1

Parking Guidance System (PLS)


Status Status (Open 1 /closed 0) 1

Tendenz Tendency (1=decrease, 2= no flow, 3 increase)

3

"Verkehrspegel by Induction Loops


Belegung_SV Occupancy freight 0


Nettozeitlucke Net time gap 35534 ms

Belegzeit Time of vehicle passing 65534 ms

Length of the vehicle Length of the vehicle 7

Street Management Belegung Occupancy 22

Street lamp with a TEU, Traffic Eye Universal



Wartezeit Waiting time 4

LOS (1 - 6) LOS 1

Figure 15: Data collected by different sensors Provided by (VAMOS Projekt 2012).

One of the main aspects in the decision to apply these technologies in cities is their influence

in the users and their costs. The effectivity of smart infrastructure measuring have been

research by (Khoo and Asitha 2016). They argument how the travel related information can be

displayed. With VMS, the information can be shown only in hotspot locations and only provide

localized traffic information. The drivers have limited response choice in their alternatives to

divert and are uncertain on the traffic conditions these alternatives offer.

The costs of this infrastructure are fully covered by the governments or infrastructure operators.

These are internalized with different instruments and are rarely paid directly by its users. The


28

costs are published yearly by the (Department of Transportation 2009). An overview of them

for different sensors is shown below:

Figure 16: Costs of smart infrastructure sensors. (Department of Transportation 2009)

In summary, the sensors of smart infrastructure are a mature technology, well recognized and

efficient. New technologies in this direction do not offer great advantages over the previous

ones. I.e. Video image processing over Radars. These technologies are highly precise and

resilient, but expensive to install and operate. Governments rely commonly in these

technologies, absorbing the associated costs. Governments use these technologies to monitor

the infrastructure, support its users and regulate them. The data gathered with this technology

is very detailed but hard to understand and use in different applications. They have limited

coverage, mainly only in major highways and key intersections, with low precision for urban

areas at a mesoscopic scale.

g. Technologies Based on Smartphones

This section focuses on the Digital Mobility services, also called Apps for mobility or Smart

mobility services. It understands and explains which services are offered braking them down

in their most important parts. It reviews its relevance in the current market and its development.

The impact of digital services on mobility has increased significantly over the last years. The

spread of smartphone and the invention of the Apps intensify this. When Apple. Inc. launched

their App store, solutions to small and different problems of life were created. An App is a piece

of software that can be run on the internet, on a computer, on a smartphone or on another

electronic device. Basically, it is a short computer program (Karch 2016)

Google trends reports the interest of a certain topic monitored through its search machine.

Since google is one of the most widely used search machine in the world, their results are

significant. When searching for the term “Smartphone apps” it is evident how the interest in

Apps has grown since the invention of the App stores in 2008. The searches for Apps through

google were recorded since years earlier, probably made by developers or the specialized

population and starts to grow exponentially from 2009 to 2012 and stayed regular for one year.

Unit Cost ElementLifetime

(years)

Capital Cost

(US$ 1000)Cost Date

Operation &

Maintenance

Cost (US$ 1000)

Cost Date

Inductive Loop Surveilance on Corridor 5 3-8 2001 0,4-0,6 2005

Inductive Loop Surveilance at Intersection 5 8,6-15,3 2005 0,9-1,4 2005

Machine Vision Sensor on Corridor 10 21,7-29 2003 0,2-0,4 2003

Machine Vision Sensor at Intersection 10 16-25,5 2005 0,2-1 2005

Passive Acoustic Sensor on Corridor 3,7-8 2002 0,2-0,4 1998

Passive Acoustic Sensor at Intersection 5-15 2001 0,2-0,4 2002

Remote Traffic Microwave Sensor on Corridor 10 9-13 2005 0,1-0,58 2005

Remote Traffic Microwave Sensor at Intersection 10 18 2001 0.1 2001

Infrared Sensor Active 6-7,5 2000

Infrared Sensor Passive 0,7-12 2002

CCTV Video Camera 10 9-19 2005 1-2,3 2004

CCTV Video Camera Tower 20 4-10 2005


29

Then the interest has been decreasing 55% since 2013 to 2016. This might indicate that the

peak has already been reached in 2013 and now it is in the process of plateau, confirming the

Gartner’s hype cycle, which describe the intensity of trends as growing curve that reaches a

peak, then decreases again and then stabilizes for the long term, growing at a slow pace

(O'Leary 2008).

Figure 17: Interest over time of the concepts of: "Apps", Adapted from Google trends (2015)

Figure 18: Gartner Hype cycle of emerging technology. Adapted from (O'Leary 2008).

Since the early 2000s, it has become possible to track mobile devices and to gather location

data of users, firstly with the phone networks (CDMA, GSM) and recently using a GPS device

in the phone. While before, this data has been exclusively gathered by city administrations via

on-street sensors, the data is now available for anyone who have access to mobile devices’

database (Zhao 2000). Subsequently, mobility and location-based services emerged on

smartphones as well as on cars. For example, Google introduced Google Maps in 2005,

offering digital maps of the whole world to smartphone users. Over the years, Google gathered

more and more data from the users to provide more specific information, location’s reviews,

Public Transport, information on the traffic flow i.e. This is possible as it only takes the phones

of 2-3% of the drivers on the road to provide an accurate report of the speeds of traffic (Herrera,

Work et al. 2010). Similar crowdsourcing approaches emerged to gather information on traffic

incidents, radar controls or pavement conditions (Yi, Chuang et al. 2015). With the increasing

spread of smartphones, further services such as car sharing and ride sharing (Teubner and

Flath 2015) or parking (Caicedo, Blazquez et al. 2012) emerged. This leads to a situation

where smartphone based services are an important influencing factor on individual mobility

Peak of inflated

expectations

Technology trigger

Plateau of Productivity

Slope of enlightment

Trhough of Disullusionment

Vis

ibili

ty

Time


30

(Wolter 2012) (Forrester 2013). As these services strive for the user’s optimal mobility, they

have a conflicting goal to ITS, which strive for a macro system optimum for example for one

city or one road (Jonkers and Gorris 2015).

When looking for granulated searches for mobility apps, some services are far more popular

than other ones. The maps and navigation Apps are more popular than the parking and traffic

information Apps Figure 19. This raises the question, which are the services the Apps provide?

How often are they used?, what are their effects in mobility?. Questions to be solved in the

forthcoming sections 3.1.3 and 3.1.4 .

Figure 19: Interest over time of the concepts of: "App transport”, "App navigation", "App traffic", "App map". Adapted from Google trends (2015)

3.1.3 Smartphone based Services

This study uses the framework for service systems to identify the services and their granularity.

This framework allows the understanding of the services systems as groups of service

modules. This section analyzes existing digital mobility services to identify their modules and

data sources. In doing so, the second step of modularization within the framework by

Dörbecker and Böhmann (2015) is applied. This step comprises the identification and analysis

of the service system’s modules. Subsequently, the data and method used for the analysis are

described, then the results are the modules and data sources of digital services for urban

transportation.

City administrations solve traffic congestion problems by operating ITS, meanwhile individuals

try to optimize their travel patterns with digital mobility services. Both groups aim for different

goals with specific resources as time, money, comfort, reliability, etc. Therefore, a large

number of heterogeneous digital mobility services have emerged for usage on smartphones.


31

Some services provide information on traffic and mobility options, such as the trip planners

Quixxit and Allyapp, others directly support the user in reaching his destination by matching

the user with bike-shares, car-shares or taxi drivers. All these services are part of a service

system for digital mobility services, which solve the complex scenario of the transportation

systems. However, the services are hardly related, the landscape of digital solutions is vast

and unstructured.

Alter (2008) defines services as “[…] acts performed for others, including the provision of

resources that others will use." In the case of digital mobility services, an “act” could be the

provision of information how an individual can get from A to B within a city most efficiently. A

service system is “a work system that produces services”, while a work system is “a system in

which human participants and/or machines perform work using information, technology, and

other resources to produce products and/or services for internal or external customers” (Alter

2011). In the case of digital mobility services, traffic data, algorithms and software applications

such as mobile Apps build a work system that produces services related to mobility.

The modularization of systems is a concept supporting the desired characteristics for product

as well as service systems, breaking down the concepts composing the system or product

(Baldwin and Clark 2000). In particular, modularization fosters co-creation of value and

innovation (Böhmann, Junginger et al. 2003). Service modularization can be defined as “(a set

of) activities being part of interactions between the components of service systems” (Leimeister

2012). Consequently, a modular architecture consists of modules and decoupled interfaces

between modules (Ulrich 1995). The interfaces facilitate co-creation of value and form the

basis of the service system. Based on service systems engineering theory, Dörbecker and

Böhmann (2015) propose a methodology framework for the design modular service systems

to followed in this work. For example, the house building industry followed this evolution. Firstly,

there were whole organizations building the whole houses. After many years, specialists in

different activities and products bloomed and the industry got modularized. This way the

industry gained quality and efficiency.

3.1.3.1 Digital Mobility Service Systems

In the vast landscape of digital solutions, modular services belonging to one service system

can add value for another service system as well (Böhmann, Junginger et al. 2003, Böhmann,

Leimeister et al. 2014). Böhmann, Leimeister et al. (2014) in their call for future research,

describe sustainable transportation as one area in which services can deliver greater value

when they are part of a service system. Then it is needed to understand the service systems

and service modules currently available. Following the framework by Dörbecker and Böhmann

(2015), the perspective of modularization of service systems is applied to digital mobility

services.

To structure the landscape of mobility services and to identify the most important modules of

digital mobility services, the modules and data sources of 54 digital mobility services (Apps)

relevant for urban transportation were evaluated as part of the scope of this study. During the


32

service analysis, it was found that different Apps provide one main service, and many different

service components. Some of the service components were directly related to mobility, some

others were not. This study helps practitioners to develop more components of mobility

services. This is also relevant for public administrations which use the traffic data they gather

to operate ITS in order to provide digital mobility services for individuals.

The heterogeneity jeopardizes deeper analysis on how these services impact mobility and how

future mobility services can be designed. Moreover, the data availability defines how far an

App can reach. Therefore, the first step is to identify the most important modules o digital

services as part of a service system. The second step is to identify their data sources.

The Apps (understood as service systems) selected for the review come from the list of 50

Apps more downloaded in the German Android Play Store’s App category “traffic” and Apple

App Store’s category “navigation”. The search was enhanced by a keyword search within a

database of more than 80,000 tech blog articles gathered from October 2015 to February 2016;

the keywords used were “traffic”, “mobility” and “navigation” to identify suitable articles. By

including tech blog articles, new services that are not yet in the top 50 Apps were included.

Altogether, 54 mobility services were analyzed in more detail. The research compared the

sample with the overview provided by Motta, Sacco et al. (2015) to identify missing services.

To identify the data sources used by the services the license files and the general terms and

conditions were revised. With an iterative coding process as described by Lacity et al. (2010),

the services were grouped into categories and grouped similar modules and data sources. The

categories of mobility services defined are trip planners, traveling analytics, car/ride sharing

services, navigation services, charging stations, location-based services and parking services.

Hereafter the understanding taken for this work is explained for each service.

Trip planners Apps provide information for the users to help them plan their itineraries within

and between cities. One example is Allyapp, it is a platform to calculate routes by several

means of transport as long as they are available on the streets. It focuses on Public Transport,

creating a partnership with the local public transport associations to get high quality and real

time data.

Car sharing services Offers cars to share and drive individually as for example with Drivy

(2016). Drivy, a French startup founded in 2010 provides a platform for individuals to rent their

cars to others whenever they do not need it. It is also called Customer to Customer (C2C) car

sharing. Drivy is little by little acquiring its competitors, after buying its German competitor

“autonetzer” in 2015 and has become Europe’s largest platform for individual car sharing

(Jacqué 2015).

Ride sharing services provide users with a platform to share rides as for example with Flinc.

Flinc is a ride sharing service that facilitates dynamic ride sharing. Instead of prearranging

rides as in traditional ride sharing, drivers get notified in real-time whenever a request of a

passenger fits their current route. While traditional ride sharing is mostly used for long-haul


33

inter-city travelling, dynamic ride sharing as offered by Flinc is also suitable for inner city ride

shares. From January 2016 on, Flinc integrates information on public transportation to

enhance seamless intermodal travelling (Schumacher 2016).

Parking services offer information on free parking lots, either of their exact position or of the

probability to find one within a specific are. Years ago they offered information of parking

facilities, nowadays these services are extended for the public streets curbside. For example,

the service parknav which is available in the US and Germany shows the probability that in a

given road there will be available parking lots (Wagner 2016).

Navigation services support the user in following a route by giving directions. For example,

Google Maps navigation feature is the most used navigation service on Android devices.

Navigation services are mostly used in-car or recently, they are incorporating navigation for

pedestrians and cyclists, and only a few provide directions in public transportation or by

different transport modes.

Location-based information services provide relevant information for the users based on

their current location. For example, dedicated services for radar controls or charging stations

exist. Apps like this can be Blitzer.de or Waze.

Charging stations services offer the information about fuel charging station either for gas or

electric based vehicles. More than their location and main services, some Apps offer additional

services, i.e. Tanktaler even offers discounts for charging in certain stations.

Traveling analytics services Monitors the behavior of the user and delivers a report of their

mobility, often analyzing it. i.e. Fleet board monitors the driving style and managing the time of

the user and reports it to one central user.

The most common services in the sample are the trip planners and the ride sharing Apps.

Followed by car-sharing, navigation and location-based information Apps. The least common

are the traveling analytics and charging stations Apps. Non-classifiable Apps were labeled as

“others”. The amount of Apps per service system is illustrated in Figure 20.

Figure 20: Categories of digital mobility services (Source: own analysis)


34

3.1.3.2 Digital Mobility Service Modules

To understand the service systems, this study goes deep in its parts, called service modules.

According to Balzert (2009), modules comprise elements that are strongly interrelated among

each other, but only weakly interrelated with elements outside of the module. Therefore,

service modules are parts of the services that generate a distinct value for the user. Service

systems are composed by many service modules. This work does not describe insights of the

technical details of the service modules since this is an analyse from the outside, it only

identifies them and their functions. Consequently, the analysis relates to the second step of

the modularization framework by Dörbecker and Böhmann (2015), covering the identification

and analysis of modules of a service system. By iteratively coding the modules within existing

mobility services.

A service module directly related to mobility addresses spatio-temporal dynamics, like routing,

dynamic map view or traffic information. A non-directly related service module is like fare

splitting, connection to calendar, service rating and trips report i.e. The services not related to

mobility are considered but not analyzed further due to its plural nature and low relevance for

mobility. Nevertheless, they seem to mark the difference on the popularity of the App.

The digital mobility service modules with an spatio-temporal nature found in the analysis are

explained:

Map view to show locations, nodes and links. In most of the cases the map is the main view,

for example, the car sharing application Drive Now shows the user’s position and all available

cars on a map on the first screen after log in, even when the users only need one car.

Route planner module provides suggestions on which route should the users follow in any

transportation mode.

Real time navigation module provides information on which path should the users take

according to their current position as a part of an already calculated route. There are unimodal

services, like Waze or google maps by foot, or multimodal, like google maps by public

transportation or Moovit.

Points of Interests (POI) show the locations of relevant activities or places in the map. They

can be thematic classified and different attributes can be added. i.e. the App Tanktaler

publishes the offers in real time in certain gas stations.

Location sharing module allows to share either the current position of the user or of any other

relevant location in the map. In both cases it is static, since it does not matter if it changes in

time or space.

Dynamic location sharing allows to share the location of a user in real time while it moves.

This is often used in taxi services as Uber or Mytaxi app, to share the position of the taxi

vehicles but it does not share yet the position of private users.


35

Parking information has become a popular service module, Pflügler, Köhn et al. (2016)

analyses the different parking information services existing and proposes a new one, based

on publicly available data. The parking information services are aided by sensors on the

infrastructure, activated by the users or predicted based on publicly available datasets.

Infrastructure Information notifies about the state of the infrastructure and any change, it can

be officially deployed through official apps, i.e. The Apps of the public transport association

notify of any maintenance or closure, besides through Waze, the users can notify of street

closures, construction sites or problems.

Traffic information service module shows the aggregated data of the traffic state for cars or

public transportation vehicles. Some of the most popular services are the one provided by

Google maps, reading the position of the users or Waze, doing the same and confirming with

reports made by the users.

Matching user to user allows the market function, connection offers to its demand for a certain

mobility service. For example, Drivy, on one side, allows private people to offer their car for

rent and on the other side, allows other people to search for an available car and rent it.

Figure 21: Services modules in Digital mobility services systems

Figure 21 shows service modules that are more often included in a specific service system

category. The map view is the most popular service module, the most of the Apps categories

include it without big variations. The Route planning, real time navigation, location sharing and

Category

Ro

ute

pla

nn

ing

Real ti

me

navig

ati

on

Dyn

am

ic

Lo

cati

on

sh

ari

ng

locati

on

sh

ari

ng

Map

vie

w

Po

ints

of

Inte

rest

(PO

I)

Park

ing

info

rmati

on

Tra

ffic

info

rmati

on

Infr

astr

uctu

re

info

rmati

on

Matc

hin

g u

ser

to u

ser

Trip planner

Car-sharing

Ride-sharing

Parking service

Navigation

Location-based

information

Charging stations

Traveling

analytics

Average

Variance 0.15 0.15 0.14 0.17 0.01 0.10 0.11 0.04 0.16 0.05

Symbols 100% 75% 50% 25% 0%


36

POI’s are the second most popular modules. Many Apps categories include them with a higher

variation. The parking information is less popular with a higher variation as well, some

categories always have them, many others do not. The infrastructure information module is

very popular in Location-based information and navigation but not in the other service systems.

The traffic information and the matching user to user modules are rarely used without higher

variations.

Other service modules identified are listed below. These are harder to analyze and less

relevant individually due to the heterogeneity and plurality. Nevertheless, they make the

difference on the popularity of an app, i.e. (Khoo and Asitha 2016) found that one of the main

factors to prefer a car navigation App is the voice guidance, above the quality of the traffic

information or displayed map.

Cancellation policy In case the user wants to cancel the inquired service

Chain of custody written evidence of service for legal purposes

Guarantee Delivery of the expected result guaranteed

Support Real time customer support

Trips report Does the App offer a report of the behavior of the user?

Save requests of the

users?

Is the App storing information of the requests of the user?

Only girls service Does the App offer a differentiated service according to gender?

Order via website Does the service works from a website / PC?

Offline mode Does the service work offline?

reminders does the App send reminders to the user and carrier?

Real time order order now?

Schedule/ Program

Order

Can users book / make orders in advance?

Messaging / call communicate with the messenger/transporter

Rating / feedback for

transporter

Key partner (carriers) rating

Client rating Can the transporter rate the user?

Personal registration do the users of both sides have to register in person at an office?

Recruiting process carriers pre-screened?

Training for drivers Do the App provide training to its key partners?

Only officially

registered vehicles

Do the providers require additional official affiliations?

App for Partners Is there a specialized Key partner App different than the one for

users?

Customize order (filter) increase fare, according to area, min. Fare, etc.

Re-routing planner Do the App can adjust the route to real time conditions?

ETA Sharing Share the "Estimated Travel Time” to arrival


37

Visual communication Provide visual information to the users

Audiovisual

communication

Provide audio visual information to the users. i.e. Map + voice,

videos.

Activity Recognition Can the App recognize the activity performed by the user?

Indoor information Does the App provide indoor maps?

Customize save your places, see what you want, etc.

Customized link to

Mobility Services

User can decide to which mobility services is the App connected

(DB Call-a-Bike or "Norisbike" or MetropolRadRuhr i.e.)

Connection with

calendars

Can the App export/Import data from the calendar of the user?

Price estimator Does the App provide a price estimator for its services?

Transfers the value Transfers the value to transport operators

Electronic ticket Can users buy an electronic ticket?

Spit fare Can users share the costs, differentiating the charge on their Credit

Cards?

Flat rates for long

distances

Does the App offer flat fares for certain routes?

Dynamic fares Flat fares for the most of the routes

Mediator in disputes Does the App official act as mediators in case of disputes

Make reports of other

kind of info

can users input reports about different issues than the App services

(infrastructure, crime, environmental issues)?

Connects with social

media

Can the App export/Import data from different digital social

networks?

Internal social network Does the App have an internal social network?

Mutes the cellphone Does the App mute the cellphone while moving to avoid dangerous

distractions?

Notification of

abnormal movement

Does the App notify you in case it detects an unexpected

movement? i.e. in case your car moves / is stolen

Provides vehicle Does the App provide the vehicle?

Different vehicles

available

Can users choose the vehicle from different options?

Float management Locate your vehicles, get reports of their trips and statistics.

Parking alert The App notifies you when your parking ticket is about to expire.

Fitness coach the App sends suggestions to keep fit the users

Pictogram Evaluate the state of the vehicle only taking a picture

Extra device needed Wearable? vehicle? other device?

Accident sensor &

Notification

Automatic Detection of accidents

Panic Button Direct connection with authorities in case of emergency


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3.1.4 Smartphone based Services Data Sources

After identifying the service systems and their service modules, the feeding of data sources for

the digital services is analyzed. As many mobility services are data-driven (Wolter 2012). The

data sources used by the services help to understand the interdependencies of services and

their modules as well as to provide an indication for practitioners of which data sources are

necessary for which targeted service.

The most popular data source used by the reviewed Apps are the device sensors collecting

information in the way a crowdsourcing. Mostly the sensors used is the GPS to locate the user

and enable the routing as well as analyze the spatiotemporal information of the user and

generate the services needed. The Google ecosystem emerged as one of the most important

data sources. Firstly, the map view provided via an API (Application Programmer Interface) is

widely used in mobility services of all categories as seen in the previous section. In addition,

google provides the routing and traffic information through an API and is widely used in

navigation and trip planner service systems. Overall, 35 out of the 54 of the reviewed Apps

use at least one of the Google API’s as a data source.

In the data set market, many players besides Google are being used by the Apps.

Nevertheless, Google is the main source for 26 / 54 service systems. Many use other private

providers, but none of them is as dominant as google is. For example, TomTom, monitors

and builds the basis for in-car navigation services. They sell the datasets and it is not

commonly built in apps, Google in comparison provides the API for this service module for

free. Another important data source are the public transportation providers. These providers

are often the Public Transport associations or parastatal companies that partner with App

companies to build their own App for the users. The datasets they can provide can be the

timetables, routes and even real time data as incidents or sudden adjustments in the network.

For instance, the App Moovit partner with the public transport providers of every city where

they are active, to offer service up-to-date in real time. Public administrations offer the data

of the traffic situation or construction works in very rare situations. The most common case is

for Public Transport changes or data sets about the urban structure of the city.


39

Figure 22: Data source popularity of usage

The different data sources used by different categories of mobility service systems are shown

in Figure 23. It can be seen that all the categories use the sensors built in the smartphone as

a data stream. The usage of information from the public administration is not widely used, and

its usage is spreader in to the different system categories. While the majority of the charging

stations services use it. None of the car or ride sharing Apps use this data source.

Figure 23: Data sources feeding digital mobility service systems

The data coming from the public administration is the focus of this work, often collected by

means of smart infrastructure. Then its review is more elaborated. The key transport-related

datasets for intelligent mobility are extracted from the data catalogue provided by the Catapult

Transport Systems (2015), only considering those owned by the public administrations. This

confirms which datasets should be shared by public administrations and build in Apps.

1. Place and space

a. Points of interest and places (POI)

b. Public Transport stops and interchange locations

c. Vehicle parking locations

d. Vehicle fueling / charging stations

e. Streets, roads and railway lines used to move people and goods

2. Environment

a. Weather data

b. Air quality

3. People, things and movement

a. Personal location data anonymously

b. Aggregated people locations data

4. Event-related data

a. Operation of transport networks (Traffic, incidents, disruptions, etc.)

b. Events as cultural, leisure, mass gatherings, etc.

5. Public Transport Services

Category GoogleOther private

provider

Public

transport

provider

Public

administration

Smartphone

sensorsCrowdsourced

Trip planner

Car-sharing

Ride-sharing

Parking

service

Navigation

Location-

based Charging

stationsTraveling

analytics

Average

Variance 0.10 0.15 0.09 0.13 0.00 0.07

Symbology 100% 75% 50% 25% 0%


40

a. Transport network capacities for every transport mode

b. Normal service’s operations as timetables and routes

6. Freight connections

a. Network wide capacity for all transport modes

7. International connections

a. Network wide capacity for all transport modes

b. Normal service’s operations as timetables and routes

8. Consumption and transaction data

a. Fare and pricing for all modes of transport

b. Opinions from social networks

c. Usage of data from different digital mobility services providers

d. Payments and purchases of goods and services related to mobility

The datasets were presented; how does this data get collected by means of a Smartphone?

The data collection techniques started using the sensors and signals of the smartphone and

have been evolving in to new techniques to measure different parameters. The transport

catapult illustrates it as shown in Figure 24 taking in account five different techniques, Manual

collection, Overt crowdsourcing (activated by the user) and Covert crowd sourcing (indirectly

taken), sensor derived (sensors inside the phone measure parameters) and service provider

generated (Network provider measure the data transfers).

Figure 24: Data collection techniques with smartphones. (Catapult Transport Systems 2015)

These different techniques make use of the sensors available in smartphones, the more

sensors the more data collection techniques and the more data that can be collected. An


41

overview of the sensors technology currently available in Smartphones is presented in Figure

25.

Figure 25: ten of the major sensors an average smartphone may be equipped with. (Matzen 2015)

Services from both technologies experience similar issues. On one side, Smart Infrastructure

offers parking information, rerouting services and traffic information, nevertheless due to its

nature, it cannot offer too many services directly to the end users and offer more to the mobility

authorities, mainly, data collection services. On the other side, smartphone services are

offered in a big variety and offer different kind of service modules. Their main data source is

the own smartphone sensors and crowdsourcing collected data but not the data collected by

the infrastructure. Both environments try to improve mobility, but in which way? Many different

institutions related to both have declared many goals and objectives, are they addressing the

global and individual optimum? Or what is their aim? That will be the focus of the next chapter.

Goals of Services for Mobility

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4 Goals of Services for Mobility

This Chapter finds the goals for smart mobility services matching the top-bottom approach with

a bottom-up approach. Firstly, checking what are the relevant organizations taking into

consideration? how they orientate their efforts. Then, the user’s needs are reviewed as a

bottom-up approach. Both will be conveyed in a structure closer to the human needs. Following

its mobility needs, uses and preferences will be reviewed with the application of a survey and

will be compared.

h. Goals of Organizations

Then, what are the goals the technology should pursue? How to prioritize the future actions?

Firstly, a global perspective to analyze, where the world should go is reviewed. In order to do

this, the Sustainable Development Goals of the United Nations (2015) were checked. Then in

a more technical perspective, what orientates the industry of the ITS and of the automotive

industry was checked. Also, governmental goals were looked through, the European

Committee for Standarization (2002) and the ITS Strategic Plan of the USA 2015, produced

by Barbaresso, Cordahi et al. (2014). A summary of the goals from different literature is

presented in Figure 26.

Figure 26: Goals mentioned by relevant sources

The Sustainable Development Goals (SDG) have a very global perspective, mostly oriented

towards environmental and health benefits. They promote initiatives and recommend policies

at different organizational levels. They promote initiatives to prevent and cure diseases related

to traffic pollution. The SDG support sustainable transportation that can address rising


43

congestion and pollution issues, particularly in urban areas, and are applicable at global,

national, local and sector levels. The policy recommendations are to be published in a global

sustainable transport outlook report that will be released on the first international conference

on sustainable transport in 2016.

The goals of the SDG standing alone do not relate directly to mobility Apps. Neither their

targets do. Nevertheless, concepts related to mobility are mentioned across different targets,

in those related to health, energy, infrastructure, cities and human settlements. The relevant

targets will be explained followed by a presentation of an extract of relevant concepts for

mobility Apps.

From the relevant goals for transport, only the 9th and 11th address Apps for mobility in some

of their targets. The 9th goal aims to build resilient infrastructure, promote inclusive and

sustainable industrialization as well as to foster innovation. The 11th goal refers to making the

cities and human settlements inclusive, safe, and sustainable. From such targets, the relevant

concepts for the goals of Apps for mobility can be identified.

The relevant goals for Apps for mobility are cited in the targets 9.1, 11.1, 11.2 and 11.a. The

target 9.1 addresses the economic development and human wellbeing, the target 11.1.

considers transport as one of the basic services to be available in households. The target 11.2

clearly regards to transport through safety, accessibility and affordability. The target 11.a takes

the path for regional planning towards economic and social development and sustainability

(United Nations 2015).

From these relevant targets for mobility apps, the main concepts of economic development,

human wellbeing, availability, accessibility, safety, affordability, sustainability and social

development can be identified. These targets do not provide specific indicators, leaving the

performance measurement open to the public. Nevertheless, these general concepts can be

considered as the goals that the United Nations (2015) recommends to follow in the

development of Apps for Mobility.

Not only the intergovernmental organization promoting international cooperation cares about

guiding the development but also the industry and industry clusters. The article written by

Catapult Transport Systems (2015) state that all transport companies are expected to be data

companies, exploiting the digital byproducts generated from their operations. Aside, many of

the major global data and technology companies are already investing in transportation

systems to explore whether they can provide enhanced services. i.e. SAP rideshare, Google

autonomous car, Apple maps. The amount of data, its degree of detail and the data analysis

capabilities of these companies make of them strong market competitors in the understanding

and offering of user-focused transportation systems.

At the Cosmos Conference on smart mobility services in Ingolstadt on March 8, 2016, the goals

to orientate the efforts of the automotive industry defined the headliners of the discussion. This

study considers what has been said on this conference as representative of the German car


44

industry. Prof. Wagner (2016) presented three strategies for mobility services, not goals

themselves, clustered in to “Product and Technology”, “Services and organization” and

“Society and communication” as shown in Figure 27. These concepts are relevant for mobility

Apps; the first cluster named technological innovation and optimization of traffic flow is included

in the “Product and Technology” strategy. The activities to add value to automobiles,

intermodality, car-free zones and digitalization are mentioned in this section of analysis. For

the “Society and Communication” strategy, the professor pointed out that the research was

meant to understand the changes of lifestyle, regional development and how the mobility

concepts became part of the created environment. These factors seemed to be current trends

to consider while developing mobility services, but not as strict goals that can be sectioned into

objectives, targets and indicators.

Figure 27: Three -column strategy of Mobility / Technische Hochschule Ingolstadt (Wagner 2016)

After mentioning the perspective of the automotive industry, what is the perspective of the

industry in ITS? The ITS are defined according to the ITS-EduNet (2005) as the following

points:

ITS integrate telecommunications, electronics and information technologies, in short,

“telematics” – with transport engineering in order to plan, design, operate, maintain

and manage transport systems.

This integration aims to improve safety, security, quality and efficiency of the

transport systems for passengers and freight optimizing the use of natural resources

and respecting the environment.

To achieve such aims, ITS require procedures, systems and devices to allow the

collection, communication, analysis and distribution of information and data among

moving subjects, the transport infrastructure and information technology applications.

It has not always been that the ITS covered such a broad part of the transportation field. They

were established in the 60’s for urban traffic and motorway control. During the 80’s, they

evolved to network approach, distributed intelligence and the inclusion of vehicles. The ITS of

today integrate different transport modes, including mobility management measures, providing


45

personalized services and increasing automation. Nowadays, the digitalization trend is pulling

them towards digitalization of services and big data analytics.

The Traffic control and Traffic Management notes of Prof. Busch (2014) at the Technische

Universität München present the objectives of Transport Management and Control under one

main goal: “Sustainable Transportation”; the general objectives were defined as following:

1. Environmentally friendly traffic

2. Safe transportation

3. Efficient transport systems

4. Comfortable travel

5. Economic traffic management

These objectives define sustainable development as the one that “meets the needs of the

present without compromising the ability of future generations to meet their own needs” (World

Commission on Environment and Development 1987). Additionally, the experts in

transportation split sustainability in three different domains, Economical, Societal and

Environmental. Such domains ponder available resources in the present and the future, but

also they do not compromise the resources of other domains without equivalent recovery or

exchange.

Governmental agencies have also made big efforts in guiding mobility services, but not yet

Apps. In this work the approaches of the European Union and the USA are reviewed, as global

economic development poles. One as a highly regulated and social market and the other as a

free market economy. The regulation norms to orientate mobility services in Europe are

European Committee for Standarization (2002) and in the ITS Strategic Plan of Barbaresso,

Cordahi et al. (2014).

The main purpose of the DIN 13816 (European Committee for Standarization 2002) is to

promote the quality of public transport operations as well as to focus interests on customers’

needs and expectations. This study expands and analyses all the different services provided

by Apps. This standard is based on a service quality loop, which compares both perspectives,

the customer’s perspective and the service provider’s perspective, measuring the difference

between expectations and reality for both, called satisfaction and performance respectively

Figure 28. The way to measure is based on a recommended criterion.

The criteria recommended is the perspective of the customer. Here it is presented as an

overview, from which the concepts relevant for Mobility Apps will be extracted. The criteria are

divided into eight categories: Availability, Accessibility, Information, Time, Customer care,

Comfort, Security and Environmental Impact.


46

Figure 28: Service quality loop of the European Committee for Standarization (2002)

The means of compliance of criteria can be also supported by App that measures the

performance or validates results. European Committee for Standarization (2002) also provide

guidance on how to measure the satisfaction and performance levels. It guides to the

application of Customer Satisfaction Surveys (CSS) for satisfaction measures as well as

Mystery Shopping Surveys (MSS) and Direct Performance Measures (DPM) for performance

levels. These data collection techniques apply stated preference and revealed preference

questionnaires as well as direct observations. Mobility Apps can be applied for any of these

data collection techniques and provide any kind of information to the user about the transport

network. The goals and conditions for a transport network that the norm measures are enlisted

here:

1. Availability: extent of the service offered in terms of geography, time frequency and

transport mode.

a. Modes

b. Network: Range and extent of the mobility services on offer by reference to

time, geography and mode.

i. Distance to b/a-point: distance to boarding and alighting (b/a) point,

need for transfers, area covered

c. Operation

i. Operating hours, frequency, vehicle load factor: ratio of passengers

carried against total capacity of the vehicle

d. Suitability: Degree on how the services fit to the transport needs of the

individual customer.

e. Dependability: Degree to which the customer may be certain that the

services will be provided as published.

2. Accessibility: Access to the public transport system including interface with other

transport modes.

a. External interface

i. To pedestrians, to cyclists, to taxi users and to private car users

b. Internal interface

i. Entrances/exits, Internal movement, transfer to other transport modes

c. Ticketing availability

i. Acquisition on network, acquisition off network, validation

Customer View Service Provider view


47

3. Information: Systematic provision of knowledge about a public transport system to

assist the planning and execution of journeys.

a. General information

i. About availability, accessibility, sources of information, travel time,

customer care, comfort, security, environmental impact

b. Travel Information normal conditions

i. Street directions, b/a point identification (boarding and alighting),

vehicle direction signs, about route, about time, about fare, about type

of ticket

4. Time: aspects of time relevant to the planning and execution of the journeys.

a. Length of trip time

i. trip planning, access/egress, at b/-a points and transfer points, in

vehicle

b. Adherence to schedule

i. Punctuality: Adherence to published schedules by time

ii. Regularity: Adherence to published schedules by frequency/ interval

5. Customer care: Service elements introduced to effect the closest practicable match

between service and the requirements of any individual customer.

a. Commitment

i. Customer orientation, innovation and initiative

b. Customer interface

i. Enquires, complains, redress

c. Staff

i. Availability, commercial attitude, skills, appearance

d. Assistance

i. At service interruptions, for customers needing help

e. Ticketing options

i. Flexibility, concessionary tariffs, through ticketing, payment options,

consistent price calculations

6. Comfort: service elements introduced for the purpose of making public transport

journeys relaxing and pleasurable.

a. Usability of passenger facilities

i. At b/a points, On vehicles

b. Seating and personal space

i. In vehicle, At b/a-points

c. Ride comfort

i. Driving, Starting/stopping, External factors

d. Ambient conditions

i. Atmosphere, weather protection, cleanliness, brightness, congestion,

noise, other undesired activity

e. Complementary facilities

i. Toilets/washing, luggage & other objects, communication,

refreshments, commercial services, entertainment

f. Ergonomy

i. Ease of movement, furniture design

7. Security: Sense of personal protection experienced by customers.

a. Freedom from crime

i. Preventive design, lighting, visible monitoring, staff/police presence,

identified help points


48

b. Freedom from accident

i. Presence/visibility of supports e.g. handrails, avoidance/visibility of

hazards, active safeguarding by staff

c. Emergency management

i. Facilities and plans

8. Environmental Impact: Effect on the environment resulting from the provision of a

Public Transport service.

a. Pollution

i. Exhaust, noise, visual pollution, vibration, dust & dirt, odor, waste,

electromagnetic interference

b. Natural resources

i. energy, space

c. Infrastructure

i. Effects of vibration, wear on paths/road/rail, etc., demands on available

resources, disruption by other activities

Mobility Apps can provide information of these fields, nevertheless they can directly act in the

fields of information, customer care, comfort and security. Within these terms, the concepts

considered are availability, accessibility, travel time, customer care, comfort, security, safety,

sustainability, complementary services and ergonomy. This is a very complete norm, that can

consider many aspects of mobility, but it is only addressed to public transport and not to other

mobility services, although the ability of application exists. Apps can not only influence all the

aspects of mobility services, but they can be significantly useful for many important aspects,

such as security, ticketing or even triggering on demand in services like Uber or Mytaxi App.

The European ITS strategic plan is called “EC ITS Action Plan” for deployment and use of ITS

suggests measures and proposals on how ITS can contribute to a cleaner, safer and more

efficient transportation system. Its goal is to create the necessary momentum to speed up

market penetration of rather mature ITS applications and services in Europe. The initiative is

supported by five co-operating Directorates-General: DG Energy and Transport, DG

Information Society and Media, DG Research, DG-Enterprise and Industry and DG

environment, approved in April 2009 (ITS-EduNet 2005).

The Strategic Plan for ITS, outlines the goals related to safety, efficiency, sustainability,

innovation and interconnection. They break the goals down into vision, mission, priorities,

topics and categories. In the latter 3, they present plans and specific objectives (Barbaresso,

Cordahi et al. 2014).

Yujuico (2015) mentions the orientation of the App developed in their research in an official

way. The government of Manila has the App development policy towards economic

development, equity, efficiency and effectiveness of the mobility. They address effectiveness

in an organizational way neither clear enough nor relevant for this research. Zegras, K. Butts

et al. (2015) reports that the digital mobility services are and should be orientated towards

economic development, sustainability, innovation optimization of mobility and digitalization of

the current industry.


49

The proposal of a general infrastructure for location-based smart mobility services by Sassi,

Marco Mamei et al. (2014) is orientated towards the concept of the citizens happiness and

well-being in any transport mode, optimizing the network in a sustainable way. Their general

infrastructure is oriented to generate more innovation in App services.

These findings are visually demonstrated in Figure 29; the most popular goal is to optimize the

mobility network and sustainability, referring to recent climate change. Some authors did not

mention essential goals like economic development, safety and quality. Other goals like

intermodality, customer care, ergonomy and interconnection are rarely considered.

Heterogeneity and inconsistency dominates in the orientation of solutions, but, should all the

authors mention all the goals? Do all of them have to follow the same orientation? Is it possible

to highlight goals that are more important or graduate them? These questions will be answered

on the bases of the match with the individual needs and preferences in the next section.

Figure 29: Mentions of goals aggregated

In order to find a match between organizational and individual goals, they must be studied as

separated groups. More than expectations, costumer needs are the drivers for mobility

behavioral decisions. These needs can be external or internal. The internal conditions do not

depend on the person, and describe more his preferences, they can be explained by the

research analysis applied to mobility by Sußmann and Roemer (2011) based on the Maslow’s

Hierarchy of Needs (Kellingley 2016). The external conditions are based on the environment

surrounding the person.

For the internal conditions, Sußmann and Roemer (2011) has made an application of Maslow’s

hierarchy of human needs applied to mobility, it has been a good approach but it still needs

more accuracy. For instance, to what Maslow calls “physiological needs”, he assigns

Availability, Affordability and Safety, which is logic, but to what Maslow calls “Self-actualization”


50

he assigns luxury needs, prestige and entertainment which do not directly corresponds.

Therefore, this work re-organizes the hierarchical structure of customer needs in mobility.

From the organizational goals reviewed, some of them can be internalized in to human needs.

Others are strictly external since they are addressed to the systems or the environment and

not to the individuals or they are merely trends, such as intermodality, reduction of car usage,

digitalization, customer care, complementary services, and interconnection.

The Maslow’s hierarchical structure of human needs, shown in Figure 30, shows the most

essential needs at the bottom and the less essential needs at the top. It classifies and

prioritizes the human needs. This varies from person to person and from organization to

organization and some give more importance to less essential needs than to others. This

depends from region, style, personal goals and experiences (Huitt 2007).

Figure 30 Maslow’s hierarchical structure of customer needs for urban mobility

The most basic needs are physiological, at the base of the pyramid. At this level, the mobility

goals into consideration are availability, accessibility and the optimization of the Mobility

network. Availability means that the mobility service will be there for that person. The

accessibility is that the service is proper so the people can use it and the optimization of the

mobility network addresses the operations of the network. In the next level of priority, the safety

needs are situated. Here the goals of economic development, safety, security and affordability

can be allocated. The economic development allows the person and organizations to earn

money and be able to exchange products. Safety is related to the avoidance of accidents and

security is related to avoid crime. Affordability addresses the capacity of the people to reach

the mobility service by monetary means.

The needs of belonging and love can be addressed by the goals of social development in

which the mobility services foster the positive social interactions of the communities. The goal

of customer care reinforces the relation customer – mobility service with trust and

communication. The needs of esteem seek to personal achievement, competency, gain

approval and recognition. The goals addressing these needs are oriented to the human well-


51

being, in which the people feel respected and satisfied, the comfort in which the people is

happy using the services, ergonomics in which the services are fitted to the people, equity in

which the people feels respected and shows respect to others.

The needs of self-actualization are oriented to the growth of the person, once they have solved

their deficiency needs. In this frame the goals of Innovation, Life-style and Sustainability can

be located. Innovation is to find new solutions to reach beyond the average. Life-style connects

with the people’s playfulness and individuality while sustainability make the people think

beyond the ego and respect other natures that surround them.

The Apps for mobility address the goals of the mobility, nevertheless in a wide spectrum as

shown in Figure 31. Different Apps address different goals, there are Apps addressing

sustainability and Apps addressing the optimization of the network, i.e. CO2go and Uber, they

cannot be integrated in to other App to create more robust solution.

Figure 31: Organizational goals matching with the Apps roles

i. User Preferences

To confirm the users’ preferences, a survey was conducted. The process to design and apply

the survey are explained with the statistical concepts. The responses are analyzed with

descriptive and advanced statistical tools to better understand the sample taken and the

relations among the responses. In this section, firstly the theory is described, then the results

of the survey are presented and explained.


52

4.1.1 Theory for Statistical Analysis

As Merriam-Webster (1986) dictionary explains, predict and forecast are synonyms: “Predict,

commonly implies inference from facts or accepted laws of nature; Forecast adds the

implication of anticipating eventualities and differs from Predict in being usually concerned with

probabilities rather than certainties.” It is very important to know how to analyze the data before

collecting it, so the surveys’ responses can be adjusted to the planned analysis. There are two

main ways to analyze them, with basic descriptive statistics and with statistical models. Since

both will be applied in this study.

4.1.1.1 Descriptive Statistics

Descriptive Statistics simply describes the obtained data. The description of the measures to

convey information about just one variable in a dataset were taken from Kuhnimhof (2014) and

include:

Xth Percentile: X% of the observations are smaller than this value.

1st Quartile: Equals to the 25th percentile, which means that a quarter of the observations are

smaller than this value.

3rd Quartile: Equals to the 75th percentile; three quarters of the observations are smaller than

this vale.

Mode: most common value among the observations.

Median: Equals to the 50th percentile; half of the observations are smaller than this value.

Arithmetic mean: summarizes the information in the data in one numeric measure. It is the

sum of all observations divided by the number of observations. Data with a very high variance

or many outliers undermine the usefulness of the mean.

Harmonic mean: summarizes the information in the data in one numeric measure. This is

especially good to represent ratios. It is calculated by taking the sample size divided by the

sum of the multiplicative inverses of each observation.

Geometric mean: This mean is especially good to represent growth rates. The quotient of

every measure divided by the previous measure (growth/change) is multiplied and then, the

nth root is applied where “n” is the number of observations.

Variance: the average squared deviation of the individual observations from the mean.

Standard Deviation: the square root of the variance. It is easier to understand since its values

are more similar to the observations than the values of the variance.

Coefficient of variation: represents the data spread as a proportion of the mean.


53

Skewness: a property to describe on which end of the range the observations are more

concentrated.

The methods to provide information about the relationship of, more than one, different

variables are the Covariance and the Correlation ρ.

Covariance: measures how two variables change together. The covariance can take values

between 1 and -1. If both grow, it is positive, if one decreases as the other grows, then it is

negative. If there is no connection between the two, it is zero. A large covariance (close to 1

or -1 means that there is a strong association in which the values of the variables spread out

strongly.

Correlation ρ: improves the information given by the covariance, calculating the degree of

association between the two variables, dividing it by the two standard deviations. This division

transforms the values in to values between -1 and 1.

There are different ways to present the data, Kuhnimhof (2014) present the Histograms, Ogive,

Pie charts, Box-plots, Pyramids, Lorenz curves. In this research only Histograms, Pie charts

and Boxplots will be used. Histograms and Pie charts are self-explanatory. A graphic

explanation of the Boxplots is presented in Figure 32, using the corresponding concepts

previously explained.

Figure 32: Box Plots explanation, adapted from Intechopen (2016)

4.1.1.2 Sample Size

The appropriate sample size should be chosen so that the amount of responses provides of

confident information about the population it represents. It represents part of the group of

people (or population) whose opinions or behavior the research cares about. To understand

sample sizes, the information provided by the survey web-tools Survey Monkey (2016),

GreatBrook (2016) and Fritz Scheuer (2016) is presented:

Population size: The total number of people in the group the research encompasses is called

the population size. It is represented by the sample size. For instance, if the researcher is

interested in the population of Mexico, the population size would be about 110 million. Similarly,


54

if the research is about a specific company, the size of the population is the total number of

employees.

Margin of error: A result from a survey is unlikely to be exactly equal the true population

quantity of interest. The margin error is a percentage that describes how closely the answer

the sample is giving to the “true value” of the population. The smaller the margin of error is, the

closer the research is to having the precise answer at a given confidence level. If the margin

of error seems to be too big, the sample size should be increased.

Confidence level: A measure of how certain the analyst is that his sample accurately reflects

the population, within its margin of error. Common standards used by researchers are 90%,

95%, and 99%.

As an example, say it is needed to decide between two different names of the product, by the

population there are 100 000 000 potential customers in the target market. It is decided that

the industry standard of 3% margin error at a 95% confidence level is appropriate, then 1,068

surveys are needed. Given that 55% of the respondents chose name A and 45% chose name

B, it is very likely that the costumers would chose name A would be in a range from 52 000

000 to 58 000 000 (55% +/-3%).

The margin error and confidence levels have a stronger influence on the choice of the sample

size than the population size. This is illustrated in Figure 33.The smaller the margin error, the

larger the sample size is needed. The higher the confidence level is, the larger the sample size

is needed. The larger the population size, the sample size is not intensively influenced.

Figure 33: Statistical accuracy of a survey. Population size vs Sample size (as percentage of population) Adapted

from GreatBrook (2016)


55

The mathematic equation of the graph above is explained here:

Where: population size = N

Margin of error = e (percentage into decimal form -for example, 3% = 0.03 -)

z-score = z

The z-score is the number of standard deviations a given proportion is away from the mean.

The right z-score to use can be obtained from several tables that look like Figure 34: Sample

size calculation - z-scores shows.

Desired confidence level

Z - score

80% 1.28

85% 1.44

90% 1.65

95% 1.96

99% 2.58 Figure 34: Sample size calculation - z-scores

4.1.1.3 Statistical Models

A statistical model is a probability distribution constructed to enable inferences to be drawn or

decisions made from data. A statistical model represents the variability of the sample using

probability distributions. Typically, it must accommodate both random and systematic variation.

The complexity of the model will depend on the problem at hand and the answer required, so

different models and analyses may be appropriate for a single set of data. The data has

variables, these can be taken as the explanatory variables, also called independent as well as

the explained variables, also called dependent or response variable. (Buechler 2007).

The selection of a statistical model depends on the question to answer and the types of values

of contained in the dataset for both, the explanatory and the explained variables as shown in

the Figure 35. Many more statistical models exist and will be created in the upcoming years

however, all of them indicate how strong is the relationship of one variable with the other ones.

Univariate analysis typically deals with the relationship between a single, dependent variable

and one or more independent variables The Multivariate analysis is different because it deals

with groups of both dependent and independent variables. The Multinomial analysis deals with

non-continuous and not ordered as well as non-numerical options (Bruin 2006)


56

Figure 35: Type of model to be used according to the kind of data. Based on (Buechler 2007) and (Bruin 2006).

Figure 35 shows three main categories, the univariate, the nominal and the multivariate

analyses. These categories will be explained in the following section to illustrate the theory of

the procedure of this work. Specifically, only the linear regression, how the multivariate differs

from the univariates, and the multinomial modelling are explained. They are also widely used

models in econometrics for transport and science.

Linear Regression:

The theoretical explanation of the Linear regression follows the description of Verbeek (2004).

Regression analysis, a statistical technique which analyses how responses to questions vary

by specific characteristics and circumstances of individuals while holding all other

characteristics equal. The key benefit of regression analysis is that it provides a better method

of identifying those factors which matter most rather than an analysis looking at the relationship

between only two characteristics at a time. By understanding this relationship, the researcher

can estimate the value of the explained variable for a given set of explanatory variables, even

if this specific configuration has not been observed in the sample. An example for this could

be the prediction of an individual’s wage given the individual’s level of schooling, experience

in the workplace, test scores or other metrics.

These variables are connected through a linear relationship of the form:

𝑦 = 𝑓(𝑥1, 𝑥2, 𝑥3, … , 𝑥𝐾) + 𝜀

= 𝑥1𝛽1 + 𝑥2𝛽2 + 𝑥3𝛽3 … + 𝑥𝐾𝛽𝐾 + 𝜀

In this model, y is the dependent variable, x1 through xK are the independent variables, also

called regressors or covariates and ε is the error term. The error term captures any influences

on y that are not explained by the set of regressors. There are several potential effects that will

influence y, such as omitted variables, measurement error or random, non-systematic noise in

the data. The β1 to βK are parameters that inform us about the size and direction of the effect

of the associated regressor. The aim in regression analysis is to estimate these parameters

Type Model Type Model

All

continuousRegression continuous

Normal regression,

Anova, Ancova

All

categorical

Analysis of variance

(Anova)Binary Binary logistic analysis

Continuous

and

categorical

Analysis of covariance

(Ancova)Proportion Logistic regression

Count Log linear models

Nominal Multi-nomial

Many

explained

variables

Multivariate analysis

Explanatory variables One explained variable (Univariate)


57

reliably in order to obtain information about the nature of the relationship between the

dependant and the independent variables.

The standard method to estimate a linear regression model is the estimation with ordinary least

squares (OLS). OLS estimation specifies a regression model that gives the best approximation

of y given a linear combination of covariates x1, x2, …, xK and a constant. For that the sample

coefficients b1, bK need to be chosen. So as to minimize the difference between the observed

value yi and its linear approximation b1x1, bKxK. Since the objective is to find the best

approximation for all observations from the sample, it can be written as

𝑆(𝑏) = ∑(

𝑁

𝑖=1

𝑦𝑖 − 𝑥𝑖′𝑏)²

By taking squares it is assure that positive and negative deviations do not cancel each other

out. This minimization problem solved in order to obtain the best linear unbiased estimator for

β, b.

Multivariate Analyses:

A multivariate statistical model is a model in which multiple explained variables are modeled

jointly. This can be explained based on the description of SAS (2016) The following equation

separate regressions represent two univariate models. So the relationships of one variable

with all the other variables is not influence by the relation of other variable for every subject.

Ç

In a multivariate setting the vectors and collect the responses and errors for the two

observation that belong to the same subject. This way a correlation can be identified. This

simple example shows only one approach to modeling multivariate data, through the use of

covariance structures. Other techniques involve seemingly unrelated regressions, systems of

linear equations, among others

Multinomial Choice Models:

Multinomial choice models can be used to analyse the choices individuals make between

options that are non-continuous and not ordered. This choice could for example be between


58

different car models and brands or a choice on commuting modes. Multinomial models belong

to a broader category called discrete choice models. These can be: Binary Logit, Binary Probit,

Multinomial Logit, Multinomial Probit and Conditional Logit among others.

The question for the researcher is then how model the probability of an individual i choosing

option j among a set of options. The individual’s decision will dependent on individual-specific

characteristics, xi, as well as characteristics that reflect the specific relation between the

individual and the option, wij.

4.1.2 Survey Design

The user’s needs can be identified according to the customer story of mobility, which every

person who wants to move from one place to other experiences. The user starts his activity at

one place at a certain time, he knows what his next activity is, either school or work, and where

it is located. How to get there? He needs information about the fastest, most reliable,

sustainable and cost effective way to get there and these aspects will shape his mobility

behavior. The trip can be divided into four parts: before the trip, at the start, transport, at the

end of the trip. In every stage of this trips, different services can be provided, they are:

Customer story Services needed

Before the trip

1. At the first part the user needs to

decide which is his next activity,

time and place.

2. Then he needs the information of

where and when a transport mode

can take him there, it can be either

car, Public Transport Unit, Bike, etc.

How much will it cost, how long will it take

Information for different transport modes (walking time, cycling stations, bus directions etc.)

Weather information

Schedule a trip

The start of the trip

1. He needs to get to his transport

mode from his current position.

2. He needs to get the key or ticket or

code or any requirement to allow

him enter the system and get the

service.

Information about where and at what time is the vehicle available

Best route to get from the current location to the position of the vehicle

Navigation to the station A

Real time Navigation

Transportation

Once in the system he needs to get to his destination, either automatically or by himself. For this he might need directions and hints.

Share dynamic location temporarily with specific persons

Panic button // fast connection in case of an emergency

Performance monitoring

Parking information

At the arrival

1. He needs to know he arrived. Navigation vehicle – destination

Trip rating


59

2. He needs knowledge on how to

move again to his next activity.

3. He needs to keep a record of the

trips.

Vehicle supervision / Panic button

Trip documentation

Figure 36: Services needed for four stages of the trip

Following the organization along the moments of the trip, the services to be provided can have

an infinity of solutions. This variety makes hard to propose and chose a specific solution.

Furthermore, the plurality of conditions in different cities or countries makes it harder even to

define the guidelines for an optimal solution. For instance, while at the end of the trip by car,

parking information is needed. There are a variety of ways to provide solutions for parking in

the market. These are mainly of three categories: prediction with public information, activated

by sensors or activated by the users (Pflügler, Schreieck et al. 2016). Therefore, it is needed

to check what is available in the market and how is it perceived by the users. A survey is the

most useful tool to evaluate user’s expectations and aid in the conception of new Apps for

mobility closer the user’s needs and global goals.

The aim of the survey is to explore the usage of the mobility services provided through

smartphone applications and how they are fulfilling the expectations of the people. To be

specific, it has the following objectives:

1. Personal modal share

2. Which is the priority of the needs to choose mobility services?

3. Which are the mobility services available in the App market?

4. Which is the frequency of usage of a service?

5. Which are the factors influencing the usage of a service?

6. Which is the degree of satisfaction of each service?

7. Which are the factors influencing the satisfaction of each service?

8. Which are the declared desires and needs of the users of mobility apps?

The objectives can be revealed when applying the following questions respectively, the

complete survey is shown in Appendix B and C.

How often do you use the following transport modes?

What are the reasons why you choose this transport mode?

The review of Krcmar and Schreieck (2016) was used as a guideline to define this.

How often do you use smartphone mobility services?

Descriptive Statistics were used to find this out.

How these mobility services address your expectations?

Statistical modeling based on a linear regression was used to find this out.

Is there a mobility App or function you want to have and have not found yet?

It also addresses the socioeconomic conditions of the respondents and served as a guide and

inspiration for this section. The World Bank used this information to create a sensitivity analysis

of the mobility situation of certain cities. From the approach of Prof. Zegras and Cottrill, Pereira

et al. (2013), the socio-demographic characteristics inspired this work. They collected this

information with an app, “the Future Mobility Survey” to generate a digital Origin-Destination


60

database for transport planning. The socio-demographic variables selected were: in which

human agglomeration lives the respondent; The age group of the respondent, these groups

were based on the categories Gender and commuting time. Additionally, smartphone

technologies’ savvy was measured as it is highly relevant for this study.

Sociodemographic questions:

1. In which city or village do you live?

2. In which age group you are?

3. Which is your gender?

4. How long is your daily commuting time (All-round-trip)?

5. How familiar to smartphone technologies do you consider yourself?

The answers correspond to every question, following a Likert scale. Each concept-question-

answer-machine langue is explained in the following section and are illustrated in Figure 37.

The first objective is to identify the “personal modal share”, it was asked in the survey how

often does every person use a certain transport mode. From the approach made by the World

Bank, the transport modes mentioned were chosen to be asked in this research. Walking,

Cycling, Public Transport, Car, Car-share, Taxi, Rideshare and Motorcycle. with a frequency

categorized as never, rarely (yearly), occasionally (monthly), Frequently (weekly) and very

frequently (daily). This is illustrated in Figure 37.

The next objective is to find the priority people set about the variables to make a mobility

behavior decision. The different approaches in the literature were analyzed and the relevant

features were applied to these questions. These approaches are the estimation of Mobility

behavior by Kopp (2015), presented at the course of Dr. Andreas Kopp at the TUM. The other

approach is the one followed by Future Mobility survey developed by MIT Prof. Christopher

Zegras (Zegras, K. Butts et al. 2015). The first one defined and fixed these variables and asked

the people for them. The other one focused on creating an Origin-Destination survey aided

with the smartphone data collection techniques. One defined and fixed these variables and

asked the people for them. The other one focused on creating an Origin-Destination survey

aided with the smartphone data collection techniques. Both examples of survey were

combined with the results of Chapter 4: Goals of Services for Mobility

, to present a list of the needs and goals to address. This list was presented to the respondents

and they were asked to prioritize them, the three most important, the three less important and

the rest six will be organized according to the multiple results received from the respondents.

To find out which Apps are currently available on the market, this research collaborated with

Krcmar and Schreieck (2016). It was more useful to use a Service-oriented taxonomy, than an

app-oriented one because of the heterogeneity of Apps providing different modular services

and not being directly related to the specific category. For instance, the journey planner by

Google Maps offers a web-based Map at the beginning, while the journey planner by MVG

offers text entries to calculate the journey. These features make them to belong to the category


61

of journey planner but not in the category of map services. These categories are fully explicated

in the section 3.1.3.

Figure 37: Variables and values explanation

In order to identify the factors influencing the usage of a service the respondents were asked,

how often they used a certain service. This question has discrete categorical answers ordered

in the way they are to be interpreted and understood by public and turned for the statistical

software into machine language.

Variable Question Subquestion

Name for

statistical

analysis

Concept For human languageMachine

language

0 - 18 younger than 18 1

19 - 24 19 - 24 2

25 - 30 25 - 30 3

31 - 40 31 - 40 4

41 - 60 41 - 60 5

61 + 61 or more 6

Male Male 1

Female Female 0

Commuting

time

How long is your

daily commuting

time?

Minutes (All-round-trip) 2_comm Commuting timeMinutes invested in mobility

dailyNumber

1 I do not use 1

2 I use it sometimes with few apps 2

3 I use it daily with few apps 3

4 I use it daily with many apps 4

5 I am an App developer 5

Location

In which city or

village do you

live?

Location city TextLocation. Aided by the geocoding

tool by Awesome Table

Coordinates

Lon, Lat

Walking

Cycling

Public Transport Never Never 0

Car share Rarely Sometimes every year 1

Taxi Occasionally Sometimes every month 2

Rideshare Frequently Sometimes every week 3

Motorbike Very frequently Sometimes every day 4

Journey planner

Navigation

Purchase Never 0

Charging station Rarely Sometimes every year 1

Parking assistance Occasionally Sometimes every month 2

Additional Info. Frequently Sometimes every week 3

Taxi Very frequently Sometimes every day 4

Journey planner

Navigation

Purchase Unknown I do not use the service Blank

Charging station Dissatisfaction It does not fulfill my expectation 0

Parking assistance Low satisfaction It fulfills a bit my expectations 1

Additional Info. Satisfaction It fulfills well my expectations 2

Taxi Over expectative Overpasses my expectations 3

GenderWhich is your

gender?gender_m

Variable / Question Values / Answers

AgeIn which age

group you are? age Selection

Selection

Frequency of

usage of

Smartphone

mobility

services

How often do you

use smartphone

mobility services?

5a_plan,

5b_nav,

5c_purch,

5d_charge,

5e_park,

5f_share,

5g_info,

5h_taxi

Satisfaction

degree with

mobility

services

through

smartphones

How these

mobility services

address your

expectations?

6a_plan,

6b_nav,

6c_purch,

6d_charge,

6e_park,

6f_share,

6g_info,

6h_taxi

Selection

Selection:

Selection:

Selection:

Smartphone

Savvy

How familiar to

smartphone

technologies do

you consider

yourself?

Smartp

Frequency of

usage of

Transport

mode

How often do you

use the following

transport modes?

3a_walk,

3b_bike,

3c_pubtra,

3d_car,

3e_cshare,

3f_taxi,

3g_rsha,

3h_mbike


62

To identify which are the factors influencing the usage of a smart mobility service, statistical

modeling based on linear regression analysis was applied. The theoretical analysis on which

statistical model to choose, is explained in section 4.1.1 Statistical testing. The dependent

variable, the frequency of App service usage, was tested with the socio-demographic variables

as independent ones: Smartphone savvy, commuting time and transport mode usage.

To identify whether a smart mobility service meets the expectations of the public, the direct

question “How these mobility services address your expectations?” was included in the survey.

Following the Likert scale (Select Statistical Services 2016), the possible answers are that the

respondents do not know the service or do not use it, no value, “Blank”, were used for the

statistical analysis of this value. Then, to indicate dissatisfaction, the value 1 is used for low

satisfaction. The value 2 is used to indicate a positive satisfaction. The value 3 is to indicate a

satisfaction above expectations of the public.

To identify which are the factors influencing the satisfaction about a smart mobility service,

statistical modeling based on linear regression analysis was applied. The theory is explained

is in section 4.1.1 of this work. The dependent variable was the question: “How these mobility

services address your expectations?” and the independent variables: Smartphone savvy,

frequency of usage of smart mobility services and commuting time.

Finally, and most important, to find out the declared needs and desires of the user, the open

question “Is there a mobility App or function you want to have and have not found yet? “was

proposed to answer, in which the user entered needs, desires, ideas and suggestions of

services to be developed.

4.1.3 Application of Survey

The survey was applied in a four weeks’ period from April 15th to May 15th 2016. Its distribution

channels were mainly online services, since the target population is people who use Apps and

the online channels are an efficient mean to reach them. The chosen channels were

professional associations (Mexican Society of Civil Engineers, Mexican Transportation

Engineers, CONACyT Mexican Scientists in Europe1), Alumni associations (Masters of

Science in Transportation Systems of the TUM2, Alumni of the Engineering Faculty of the

UNAM3, DAAD Scholarship holders4), Websites of Non-Governmental Institutions (Planeacion

y Desarrollo, Laboratorio para la Ciudad de Mexico) among other advocacy groups.

1 : CONACyT Stands for the Mexican Minister of Science and Technology, “Consejo Nacional de Ciencia y Tecnología”. 2 TUM stands for Technische Universität München 3 UNAM stands for National Autonomous University of Mexico 4 DAAD stands for the German Agency of Academic Exchange. “Deutsche Akademische Austausch Dienst“


63

The survey is an online questionnaire composed on nine questions and lasted about four

minutes. During its application it was monitored with a map and dynamic graphs of the

information collected Figure 50.

The whole website can be found in Appendix C. The Evaluation criteria of the Committee on

the Use of Humans as Experimental Subjects (COUHES 2015) of the Massachusetts Institute

of Technology (MIT) is used to perform this survey. Although the COUHES has not been

involved in this procedure, it has been taken and applied as a reference due to the following

reasons. Firstly, The COUHES framework is considered a fair procedure for the interviewer,

its institution and the respondents. All of them can be sure that their data will be accurate and

safe, the data will be collected in an unbiased way for the aims of the institution and the

respondents won’t be annoyed as well as their data will be kept safe. it is a well-known

procedure for the author, since he has applied it successfully on the study Zegras, K. Butts et

al. (2015). Thirdly, the COUHES procedure has been used to produce vast high quality

research for the Massachusetts Institute of Technology, with successful results. Based on the

COUHES guidelines, the following survey will be performed. In such a way 390 responses

were collected.

Before its application, the mentors of this work, peers of the author and a trail in the field tested

the questionnaire on different sectors of the population (young, old, people in early career,

adults, women and men). It has been found that the first objective of the survey, the priority of

goals for the people, was too complex, they did not understand it well and they have to invest

more than two or three minutes into it, skip it or leave it partially answered. To guarantee the

completion of the survey by respondents, it was decided to leave it out of the questionnaire.

4.1.3.1 Preparing Statistic Values

The population focus of this research is people using the internet, smartphones and

smartphone based mobility services. It was applied all over the world, since the smartphone

based mobility services are not strictly location based.

Let’s estimate the number of the population in focus. It can be said that every smart mobility

service user has a smartphone since they are needed to use these services. Also that every

smartphone user is an internet user since the most of the smartphone services are based on

internet services. Then the focused population for this research is smaller than the people

using smartphones and the people using the internet. The world has currently around 3.4 billion

people using the internet. Its geographic distribution per continent can be seen in


64

Figure 38: Internet users per continent. Since the most of the responses came from Europe

and south America, this research can focus in these regions. From the Figure 18 it can be

concluded that these regions account for the 21.8% and 19% of the internet users respectively,

summing 40.8% of the total of internet users. 3.4 billion * 40.8% = 1.38 billion people (Internet

Live Stats 2016).

Figure 38: Internet users per continent (Internet Live Stats 2016).

The smartphone users are also an important number for this research. According to Statista

(2014) there is a smartphone use penetration in percentage of the total global population of

25.3% in 2015. GSMA reports a market penetration of 78.9% for Europe, 70% for North

America and 52.3 for Latin America in 2014 (GSMA 2015). Since the population of these

regions is 7.35 billion for the globe, 0.739 for Europe, 0.641 billion for Latin America and 0.360

billion for North America. The smartphone users’ population in focus can be estimated as in

Figure 39, reaching a total of: 1.22 billion people. This is less than reported as the estimated

amount of internet users and is considered valid for this research.

Region Smartphone penetration

ratio

Total Population

Smartphone users

[%] [billion People] [billion People]

Europe 79% 0.739 0.583071

North America

70% 0.641 0.4487

Latin America

52% 0.36 0.18828

Sum for Population Size: 1.22


65

Figure 39: Estimation of population size

The sample size for the population focus can be calculated following the procedure explained

in Sample Size. For a population of 1.2 billion people and a confidence level of 95% and a

margin of error of 5%, the sample size needed is 385 responses. For a population of 1.38

billion people, the same sample size is reported. In both cases a normal distribution of 50% is

used, this means, that the optimum sample size is the calculated number or even less, since

this normal distribution is the worst case and 385 responses is enough.

4.1.3.2 Monitoring of Survey Application

The data collected was dynamically analyzed and presented at the website shown in A. The

geographical progress of the survey’s collection can be seen in Figure 40: Progress of survey's

collection. Descriptive statistics of the gender distribution, age distribution, commuting travel

time and date of response for both versions, English and Spanish, was collected. The dynamic

monitoring of the data collection was applied to encourage people to take an active part in the

survey. When they see that not so many people from their region have answered the survey

yet, they will be more willing to take it. This was reported in emails and messages sent to the

author during the data collection period. The amount of responses collected per day can be

seen in Figure 40.


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Figure 40: Progress of survey's collection from April 15th to May 15th 2016.

4.1.4 Results of Survey

4.1.4.1 Descriptive Statistics

From the responses obtained, the sample of 390 is statistically valid with a confidence level of

90% and a marginal error of +/- 5% to represent a population of 1.3 billion people. The

descriptive statistics of the sample show that slightly more men than women answered the

survey, with 53% and 47% respectively. The age distribution of the sample is pretty similar to

the distribution of the population of the global internet users provided by Statista (2014). The

most of the users were between 15 and 34 years old, the second biggest group is of people

between 30 and 44. The least of the people belong to the groups older than 40 years. The

people of the sample reported in average a daily usage of smartphone with some Apps (more

than few and less than many); nevertheless, the three first quartiles report a daily usage, from

few Apps to many Apps. Developers are out of the three first quartiles but they are more than

people who do not use a Smartphone. See

Figure 42: Smartphone savvy level reported.


67

Figure 41: Gender distribution of respondents

Figure 42: Smartphone savvy level reported

Figure 43: Boxplot of the reported commuting time

Figure 44: Age distribution of the sample

Figure 45: Distribution of internet users worldwide as of November 2014, by age group (Statista 2014)


68

Figure 46: Average daily travel time as a function of GDP per capita (Schäfer 1998)

Figure 47: Frequency of usage of transport mode.

From the sample, the reported transport mode usage provides useful knowledge. The results

can be found in Figure 47. The most widely used transport mode is the public transport with a

weekly frequency from some times per month to everyday mainly. The next more popular

transport mode is the car, with an average of usage every month between weekly and yearly.

This finding denotes two things, firstly people use the Public Transport more often than the

car, and the car is not being used frequently.

The respondents reported to walk with a frequency from some days per week to never although

people walk every day. Only the percentile between the 75th and 95th of people understood

this phenomenon. Such an answer denotes how wrong the concept of walking as a transport

mode, pedestrian behaviour and non-motorized infrastructure is, and therefore it is often


69

overlooked. Unless there is a disability to walk, the frequency of walking should be 95% with

some outliers as people in wheelchairs. Cycling is also present as a transport mode growing

in importance, the sample reports cycling with a monthly usage between never and weekly.

Cycling has almost caught up the car usage, outstanding its importance as one of the main

transport modes and not an “alternative transport mode” anymore.

The least used transport modes are Taxis, Car-share, Ride-share and motorbikes. People

chose the Taxi on a yearly basis in average, between never and monthly. Nevertheless, the

responses between the 75th and 95th percentile reported a more frequent usage up to a daily

usage.

Car sharing is being used on a low yearly basis in average, between sometimes per year and

never. The responses reach a monthly usage in the percentile 95th and outliers reported to use

it on a weekly basis. Ride sharing is reported in average usage less frequent than yearly,

between never and sometimes per year. Its 95th percentile reach only a monthly usage,

nevertheless some outliers use it on a daily basis.

Motorcycle is rarely used. It is reported in average between never and sometimes per year

with the 95 percentiles in never. Some outliers use it on a yearly, monthly, weekly and daily

basis. This denotes the motorcycle as a very specific and uncommon transport mode for the

sample.

Then the idea of the car as an everyday transport mode is questioned towards a more

multimodal behaviour in a week scope, in which people use certain transport modes at some

days, and other modes in other times depending on their activities. Instead of different transport

modes for the same trip. Furthermore, few of the respondents associated walking with the

usage of different transport modes. This can be a big explanation to the general overlook of

the pedestrian infrastructure, regulations and general attention.

The frequency of smart mobility services usage was also evaluated; its results are presented

in Figure 48. The journey planners were indicated as the service of planning before the trip

(which stations, lines, transfers, costs, etc.) - e.g. MVG, BVG, Google maps -. The journey

planners were the most widely used services, with an average on a weekly basis. Some

outliers use them yearly or do not use them at all. The navigation service, indicated as real-

time directions - e.g. Google Maps, Tom-tom, Waze -, is being used on a weekly basis by the

sample. Its usage ranges from monthly to weekly, with some people using it daily or yearly

(Figure 48).


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Figure 48: Frequency of usage of Smartphone based mobility service

The service to purchase tickets, MVG, Deutsche Bahn, Moovel, etc. present a yearly average

usage, from the 1st to the 75th percentile the range is from never to on a monthly basis. To the

95th percentile a daily frequency is reached. Both the service of information of charging stations

- e.g. Chargepoint, Tanktaler, BMW Charge now- and the parking assistance services

(location, reservation, prediction, etc.) - e.g. Park pocket, Parknav, Parknow – have a similar

frequency of usage. They represent a low usage of nearly never. Some outliers reported to

use them on a yearly, monthly, weekly and daily basis. Ride-, Car-, and bike sharing Apps E.g.

Carpooling, Blablacar, Nextbike, Car2Go, Ecobici – are reported as not often used. Their

usage ranges from never to sometimes per month with outliers using them also weekly and

daily. Apps that provide additional information for transport (traffic, infrastructure, locations,

etc.) - e.g. Waze, Blitzer, Stau info – are still rarely used, with a monthly frequency or fewer,

but still the 75th percentile reach a daily usage. A similar frequency is observed for taxis ( Figure

48).

The service satisfaction presented a more similar distribution among the services (Figure 49).

The Journey planner and navigation presented an intense concentration of the values in

satisfaction, with the three first quartiles and the mean value set at “satisfied”. They were the

highest evaluated services together with taxi services. Taxi services were symmetrically

distributed with the mean value on “satisfied”. The services to purchase tickets and taxi

services represent a slightly higher satisfaction than the services of charging stations, parking

assistance, Ride-, Car-, Bike- Sharing and additional information. Nevertheless, all of them

reach values between satisfaction fulfilled and low satisfaction. The fact that none of the

service reached a satisfaction level closer to low satisfaction than to above expectations can

be attributed to the feeling of play the “hard to get” of the people, where they express being

less satisfied than they really are. Another explanation can be that they got used to the services

after a long time using them and now they are not surprised anymore (Figure 49).


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Figure 49: Satisfaction with smartphone based mobility services

The high usage of route planners and navigation Apps is significantly higher than the usage of

the other services. This situation might be due to three factors, the frequency of the necessity

to use the services and the maturity of the technology. Route planners have been in the market

for already more than five years while parking Apps are still in development. Besides, the

availability of such services might be more common in cities with well-developed public

transport system than in cities without them. Since the Apps can easily permeate through

countries, the degree of connectivity the mobility services of city have with the internet, or how

“smart” the mobility of a city is seeming to be more relevant.

The taxi as a transport mode is not widely used but their Apps present a high degree of

satisfaction. This can also be reflected in the rapid grow of companies like Uber, Lyft and

Cabify. In the following section, the services they provide will be analyzed to identify if the

additional services play an important role.

Services like trip planning and real-time navigation are highly popular and highly satisfactory,

but there is still work to do. People’s expectations are not overpassed yet and in the comment

section the battery demand of the GPS, preciseness of GPS and transport routes or the

existence of the service is pointed out, which seems to be available only in well-developed

cities.

4.1.4.2 Geography

The geographical positions of the responses were also collected in the survey. The spatial

distribution of the responses is concentrated mainly in Germany and Mexico, many of the

responses came from other countries such as France, Spain, Italy, Chile and the United States.

Few responses came from other places in the world. Therefore, this research concentrated in

the population of Europe and the Americas (Figure 50). Since the smartphone-based mobility

services are not strictly location based Apps can be found and used independently of the


72

country, or city, their spatial distribution is taken only as an incentive to get more answers from

different places of the world.

A dynamic map that tracks the location of the responses in real time was built to make the

research more interesting. The respondents answered the survey in google forms. The google

form has an “Add-in” to geocode the keywords in the location column, in this case location.

The Add-in transforms keywords in to coordinates for Longitude and Latitude. It is provided by

the company called “Awesome Table”. The form is connected to a google fusion table that is

updating it in real time. The google fusion table is being read continuously as a database by

CartoDB. CartoDB creates the map. The map’s HTML code has been embedded in the website

of the survey. See Appendix C: Survey Website.

Figure 50: Geographic Distribution of the responses

4.1.4.3 Linear Regression

To test what people are more likely to use which services, a linear regression was used through

the software R. The linear regression is a tool considered strong enough to identify the

relationship within variables even if they not continuous but categorical. The experiment is

mathematically explained as:

Frequency of use of each service ~ Smartphone Savvy + Commuting time + Modal choices

Different variables influence the usage of different services. It has been found also that not all

the correlations meant causation; therefore, it is needed to rethink their relationships. In all the


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cases, the linear regression presented a good fit. In none of the cases, the commuting time

influenced the usage of an App.

For each experiment, two conditions are checked. Firstly, how well the regression fitted to the

experiment, also called goodness of fit. Secondly, how strong the relationship of the dependent

variable with the independents is. To check the goodness of fit, two conditions were checked,

firstly how well distributed the residuals around cero are and the R-squared or overall model

fit, the smaller the better. Then to check the relationship within the variables, the “Estimated

coefficients”, “t values”, Standard error and “Pr(>|t|)” – calculated probability- of every variable

were considered. The estimates define the equation of the regression line and show how

strong the relationship is.

When evaluating the factors influencing the usage of a journey planner (X5a_plan) it was found

that the variables of smartphone savvy and the frequent usage of public transport had a strong

influence on it. To check for a good fit, the residuals and the R-squared value were checked.

The residuals (Min, 1Q, Median, 3Q, Max) proved to be symmetrical around cero. The R-

squared value is 0.2922 as an overall fit, which means that the linear regression presents a

good overall fit for these variables. When focusing on the relationship within the variables, the

biggest estimated coefficients were Smartphone savvy (Smartp) and Usage of Public

Transport (X3c_pubtra) with 0.565 and 0.218 respectively). Their t values are also far from

cero (8.81 and 4.04) and their calculated probability (Pr(>|t|)) is very small. These conditions

indicate a strong relation between these variables. Also the software R indicates a strong

relationship is with ***, **, *, ,(in a decreasing order) at the right side. The other variables do

not show a relationship with the dependent variable.

Call: lm(formula = X5a_plan ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -3.7468 -0.5490 0.1758 0.7813 2.1346 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1790773 0.3370600 -0.531 0.596 Smartp 0.5655547 0.0641892 8.811 < 2e-16 *** X2_comm 0.0009234 0.0021620 0.427 0.670 X3a_walk 0.0366868 0.0578974 0.634 0.527 X3b_bike 0.0724870 0.0411305 1.762 0.079 . X3d_car 0.0100924 0.0462439 0.218 0.827 X3c_pubtra 0.2189751 0.0541215 4.046 6.52e-05 *** X3e_cshare -0.0550128 0.0806510 -0.682 0.496 X3f_taxi -0.0155837 0.0590562 -0.264 0.792 X3g_rsha 0.0215203 0.0751882 0.286 0.775 X3h_mbike -0.1190209 0.0794651 -1.498 0.135 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


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Residual standard error: 1.083 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2922, Adjusted R-squared: 0.2704 F-statistic: 13.38 on 10 and 324 DF, p-value: < 2.2e-16

Following the similar procedure, the analysis was conducted for each smartphone based

mobility service. A summary is explained in this section. The full report of the results can be

found in Appendix D: Programming Code in R for Linear Regressions and Appendix E: Results

of Linear Regressions

For the navigation service (X5b_nav) it was found a good fit of the linear regression to the

model. Other variables have also strong coefficients but their P-value was reported too big and

therefore they are not marked with the star. Only the usage of Smartphone and the car were

strong independent variables influencing the usage of a navigation. This might be clear

because the most of the navigation services are dedicated to driving, there is no need in

navigation in Public Transport, Cycling or Walking.

For the services to purchase tickets (X5c_purch), the R squared value of (0.113) is small but

the residuals are not distributed symmetrically around cero. Then the fitness is not so good.

There was also found a strong relation between the variables, smartphone savvy (Est. Coef.

=0.23, t value=3.47) and taxi usage (Est. Coef. =0.21, t value =3.41). However, in the reality,

the usage of taxi has no relation to the preference of App usage to purchase tickets in different

transport modes. Then such kind of influence should have another explanation.

For the services of information of charging stations (x5d_charge), the R-squared value of

(0.077) is very good but the residuals are not symmetrically distributed around cero. None of

the variables presented favorable values in their coefficients, t-values nor P-values for a

correlation. The usage of taxi (X3f_taxi) presented some hints of a correlation and is marked

with two stars **. When analyzing what can be the relationship between taxi usage and

information of charging stations there is not much sense. Therefore, although there is a

correlation, there is no relation. “Correlation is no causation”.

Similar results presented the analysis for the services of Parking Assistance services

(X5e_park). A good R-squared value but no symmetry within the residual, then a poor

goodness of fit. Only favorable values for a correlation with the usage of motorcycles which

makes no logic.

The analysis of the influences of rideshare has shown a positive goodness of fit with an R-

squared value of (0.291) and residuals slightly symmetrical. The variables Smartphone Savvy

(Smartp), usage of a bicycle (X3b_bike), usage of car, usage of taxi (X3f_taxi) and usage of

rideshare (X3g_rsha) presented favorable values for a correlation, Nevertheless, only the

smartphone savvy, the usage of a car, and the usage of ride share make sense. This indicates


75

that the people practicing car-sharing and ridesharing are highly likely to use an App for these

services.

The usage of services to acquire additional information (X5g_info) present a good fit with the

linear regression (R-squared=1.30), Residuals symmetrical enough. From the independent

variables, only the Smartphone savvy and the car usage have a strong correlation with the

usage of information Apps. This can be caused because the most of extra information is for

cars, like traffic, the position of traffic control cameras or the infrastructure state. In the declared

answers people indicate an inexistent service to monitor the state of the infrastructure or the

traffic state for bicycles or pedestrians i.e.

For the Apps providing the service of taxi hailing (X5h_taxi), the R-squared value of 0.337 is

too big but the residuals present symmetry around 0, then the fit can be considered good

enough. For this analysis, the independent variable, Smartphone (Smartp) and taxi usage

(X3f_taxi) presented a correlation. It is clear that the more someone use a taxi (or Uber), the

more they are willing to use an App for that.

4.1.4.4 Declared Preferences

To be more specific for the services the people really want, they were asked to report their

preferred service through smartphone Apps. They responses have been read, conveyed and

summarized in the following statements. Figure 51 presents an aggregated list of these

responses.

The term “More cities” were taken when the people expressed they would like to have in their

city a service that already exists, or that they would like to have many cities covered with the

same service.

The bus is often considered as ignored by Apps. Their lack of information and lack of precision

can be translated in to a low reliability of the bus networks. The term Multi-modality was

assigned when people expressed they wanted to have more transport modes integrated in to

their apps, like cycling, Public bicycles or taxis. Buses were often called and therefore they will

have their own term.

This is what the people express they would like to have. The people is not an expert so their

answers are merely indicators of the possibility of success of a strategy and not precisely what

should it do. Some of these services already exist. A local platform indicating what works and

what does not, would propose the best services for each location.


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Figure 51: Modular services as responses of declared preference.

Services are highly interconnected; therefore, it is hard to locate suggestions only to one

service while it addresses many service modules. For example, people said Bus information

and navigation. This would address the Route planning service, the map view and the

navigation services.

One of the biggest barriers or limits of Apps are the data sources. An App can reach what the

data allows it to. The people might wish it, the organizations consider it; but the conditions set

by the data will define the limits of the development of the technologies.

Analyzed

service

related to:

Response concept Freq.Already

exists?

Bus information 4

Integrate a delay alarm 1

Integrate possible delays and

warnings1

Journey planner multi-modal and

multi-city for long distance traveling1

Multimodality 5

Preciseness 4

More cities 8

Integration of energy consumption

and reliability1

Frequency based shortest paths 1

Bus information 2

Parking in critical areas 2

Multimodality 3

Preciseness for addresses 1

Proper cycle lane indicators 1

show routes of ride share offers 1

Easier carpooling apps 1

all car sharing services in one app 1

higher quality 2

Motorcycle share 1

Accidents and traffic jams 1

Location based probability to have an

accident 1

Location based probability of crime 1

Route tracker and predictor 1

Multimodal navigation 7

Multimodality 1

Bus information 1

Cycling navigation 3

Pu.T. Navigation 3

Speed recommendations 1

App versions for disabled people 1

Multi-Lingual services with local

major dialects / translations 1

Orientation for Rural villages 1

Traffic flow in real time 1

Integrate with official information of

traffic 1

Acc

ess

ibil

ity

Traf

fic

info

Car

-/B

ike

-, R

ide

-

shar

ing

Nav

igat

ion

Ro

ute

pla

nn

ing

Map

vie

w

Loca

tio

n-

bas

ed

info

rmat

ion

Analyzed service related Response concept Freq.Already

exists?

Points of Interest (POI) Preciseness for addresses 1

Parking information Parking information 3

Dynamic Location sharing Preciseness 1

location sharingDynamic locations of

public transits vehicles1

Matching user to userCycle-share and Walk-

share / companions1

Charging stationsGas stations + additional

services (Restaurants, etc)2

Traveling analytics Freight transport tracking 1

Other responses related

to:Response concept Freq.

Already

exists?

Walking Safe walking app 3

Cycling app 3

Cycling app integrating

safety1

Routes, Workshops, Shops

for cycling1

Safety and securityIntegrate safety and

security in the algorithm5

Integrate weather

information2

Connection with

emergency services 1

Autonomous car hailing 2

Cable cars in the alps 1

Cycling

Integration

Future

Comparison of local and

international bus and train costs1

Illustrated train seating-maps (with

advises) - similar as SeatGuru for

aircaft seating

1

cheapest flight tickets without

indication of destination (Like in Ryan

air)

1

multi national and multi-modal

planning/ booking/ Paying/

Navigation app

1

To enter and generate O/D

information2

Low carbon mobility 1

Apps with low Battery consumption 3

Wi-Fi in transport 1

Location more precise than GPS 1

Pre

sen

tH

ard

war

e

Services and Data. Smart Infrastructure vs Smartphone Apps

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5 Services and Data. Smart Infrastructure vs Smartphone

Apps

This chapter conveys what is known about both service systems environments, smart

infrastructure and mobility services via smartphone. Firstly, it sets an economic framework

through the market size and the popularity of smartphones. Then it goes to the factors of

success of the market penetration of digital mobility services and the goal orientation their

users follow. A special focus is given to the privacy issues and the capabilities of influence of

each environment. Additionally, an outlook for data collection techniques and companies is

presented.

The emerging Intelligent Mobility Market is composed of activities dedicated to move people,

goods or data in an environmentally friendly and efficient way using digital technologies.

According to Catapult Transport Systems (2015) its market is about to grow from its current

annual value of € 167 billion around to € 1.07 trillion euros per year in 2025, it means it will

grow 6.5 times bigger in ten years. Therefore, research, development and innovations have

big opportunities in this field.

Transport and mobility companies are turning in to data analysis companies. Researches have

explored the application of IT within ITS’ solutions by means of Apps and different IT products

and services. Further than apps, Vishwanath, Gan et al. (2014) design a mobility model for

urban transportation that leverages developments in the IT sector. The science in ITS is

evolving, the internet of things creates the term of “Smart Cities”, developing “smart mobility”

services, new concepts are being developed focused on people, vehicles, infrastructure and

devices (Dimitrakopoulos and Demestichas 2010, Wolter 2012). Kapoor, Weerakkody et al.

(2015) suggest digital solutions to foster citizens’ involvement in transportation decisions and

(Cottrill, Pereira et al. 2013) designed a tool to monitor citizens to track their mobility behavior.

Many of the recent ITS developments are based on large amounts of data from different

sources, making ITS a more and more data-driven discipline (Zhang, Wang et al. 2011).

Continuous GPS based data offer greater than 98% correlation across different roads, traffic

and weather conditions. GPS can be highly battery demanding, regular drivers are rarely likely

to have their phones plugged in their charger, but sensor based techniques combined with

map and crowdsourcing data can achieve more than 94% of correlation. Smartphones can

offer reliable speed, location estimates and furthermore, other contextual information

(locations, SMS reads, email distractions), therefore, they can be considered as super-set of

On-Board Diagnostic Devices (Meng, Mao et al. 2015). Several pilot projects worldwide have

demonstrated the technical feasibility of FCD (from cellular phones) with globally good results

compared to traditional collection methods (Leduc 2008).

ITS started in the 80’s and they are popular in industrialized countries. ITS require large

investments and political stability for infrastructure decision. The digital mobility services

deployed through Apps started in 2008 and now they are a key aspect of any mobility service.


78

Their only dependence is the smartphone ownership of the public and its capitalization to keep

the Apps running, which is not high. This is a small investment taken by the users and not by

governments. Users are willing to acquire the services at low costs. This way, it can easier

penetrate in the markets.

The declared responses from the user collected through the survey were analyzed, quantified

and compared with Maslow’s pyramid of human needs. This is shown in Figure 52. This

analysis shows that they are mostly focused on the upper categories. These categories are

the less basic and essential for the human being, however, they caught a significant attention.

The full quantitative analysis is shown in Appendix F: Goals addressed by declared service

responses.

Figure 52:Goals and Needs Addressed by Declared Responses from Users.

In terms of coverage, the Smart Infrastructure sensors have proven limited local areas of use,

so that a huge number of devices must be installed to determine the traffic situation in a wide

area. This situation is eased with smartphones, since they are there where people is located,

so the most popular areas will be well monitored and the less demanded areas will have less

measurements. It only takes the phones of 2-3% of the drivers on the road to provide accurate

velocities of traffic (Herrera, Work et al. 2010). Figures by Forrester (2013) show an individual’s

mobility behavior is more and more impacted by digital services used mostly via smartphones.

For example, Google Maps was nowadays the 6th most used App in 2015, offering routing,

navigation and a variety of additional features for individuals travelling within or between cities

(Jonkers and Gorris 2015). And the popularity of the smartphone is intensively growing as

Figure 53 shows.


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Figure 53: Percentage of adults who reported smartphone ownership, and its change between 2013 and 2015 in selected countries (PEW Research Center 2015, World Economic Forum 2016).

It is proven that there are more diverse data in smartphones than it is in ITS, although the

smartphones have only a few sensors, the services they provide are often combining the data

from different kinds and sources. These sources are also more fragmented, namely the owners

are from different natures (Governments, other companies, NGO’s, etc.). Also the data used

to provide services through smartphones is considered to be more sensitive, since the degree

of consciousness the users have over its source (their phones) is high as well as their

understanding of the phone’s Apps is low. They have the feeling that the Apps will take other

sensitive information more than what they need. Sadly, they often do. For instance, a map

application request access to the contact list of the user; which does not make sense for the

services they provide. The smart infrastructure collects information with different sensors and

these are a well mature technology, with highly standardized data formats that rarely changes.

They belong to the governments, collect information of the vehicles or people passing by and

nothing else; therefore, the mistrust in them is lower.

For the services provided, the Smart Infrastructure provides basic services, decided often by

a top-down approach and far from what the users want. Clearly, the most of them are provided

by the governments. On the side of the smartphones, these services are vast and

heterogeneous, they provide services very close to the customers and easy to understand.

Often they are regional, so their services adjust very well to regional conditions and can be

adapted for an international deployment but are not precisely inter-regional services. Once the

smartphone is popular all over the world, the Apps can be used in many other places.

The analysis of the service systems of ITS and Apps applied was based on Busch (2014). He

analyses main capabilities of information for transport as the scope of influence, the type of

information sent, the type of regulation and if the information is in real time. This analysis shows


80

that both service systems environments, the ITS and Apps, can have similar capabilities,

depending on which service systems is in focus (Figure 54).

Figure 54: Categories of services provided by ITS and Apps. Own elaboration with information from Busch (2014)

The Traffic Management and Control Systems can influence the movements of the travelers

before (pre-trip) or during the trip (on – trip) through three levels of intervention:

1. Information

2. Recommendation and guidelines

3. Regulation and control

Two main differences arise. Firstly, that the type of information sent is highly individualized

through Smartphone Apps in comparison with ITS. The reason can be simply how close to the

user’s the smartphones are. Secondly that none of the Apps is being used for regulation

functions. This can be again due to the privacy concerns of the data being transmitted through

the smartphones. The contact between the user and the smartphone is way closer than with

any transport vehicle, namely car, bus or train i.e. therefore the phone is able to measure many

other variables relevant for mobility like perceptions or opinions rather than only vehicles

passing. Besides, it can provide a communication channel in three directions instead of only

in one. Namely, the user can receive, enter and share information.

These levels of intervention are also covered with Apps and smartphones. Until now 2016,

Smartphones provide information, recommendation and guidelines but no regulation and

control App has been found. The App environment is still too young, free, un-regulated. IT has

not been seen a service trying to penalize users. Since the smartphones can access so much

information about its owner like, the places visited, conversations and interests, any

intervention with them is highly sensitive. In very critical cases the police can have access to


81

the cellphones, only under the permission of a judge and under strong control. This rarely

happens and is highly polemic. In Europe the terms of data protection are stated in the personal

data protection laws (European Comission 2016).

The data collection techniques are very different according to the technology of the sensors

and the aim of the data. New techniques are being developed to collect the data with

smartphones and they are merging datasets, clarifying or drawing new conclusions after

collecting different datasets. For instance, Lee and Gerla (2010) focused only on Vehicular

Sensing Networks, combining data collected and processed from cars (FCD) with the data

collected by embedded sensors as well as Leonhardt (2008) developed a machine learning

algorithm to merge data from Floating Car data and Smart Infrastructure.

Digital mobility services often start like a small start-up and then take capital from major

technology companies until they are absorbed by them. This situation makes the market harder

for small enterprises who want to grow independently. Moreover, the expectation of no-cost

from the users makes it harder for developers. Users expect the services for free or at a very

low cost. Half of iOS programmers and 64% of Android Developers operate under a “Poverty

line” of US$ 500 per App per month. This “expectation of free” has been created by the

crowdsourcing business models deployed successfully by major tech firms for the big scale

but they do not apply for smaller providers. Currently, the attitudes to personal data privacy

and concentrations of market power and legislation slow the potential threads these business

models can do to the industry (Catapult Transport Systems 2015).

In Summary, the intelligent Mobility Market is a huge market and is going to grow intensively

in the next ten years. The solutions based on Smart Infrastructure have proven to be a precise,

resilient, and trusted while the solutions based on Smartphones are unstructured, de-regulated

and variated. The latter grow and expand rapidly and at low costs with lack of trust from their

users. The cooperation of Smart Infrastructure and Smartphone based Apps can be an efficient

way to convey the successes of each one and overcome their limitations.

Solution: A Platform of Modular Services

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6 Solution: A Platform of Modular Services

Many services are available in the market. Many are not popular but they are very useful.

They might not be practical since they are only one single modular service, instead of a service

system. Nevertheless, as shown in the service analysis, service systems are also fragmented

and spread out, namely, they provide different modular services. For instance, if the user wants

to calculate a route by public transport he might use google maps, then if they want to be

navigated in their trips he will have to change to Moovel.

Services provided by Apps are very popular but solve basic issues of mobility with complex

programming programs. They are mostly service systems offering many modular services.

More specialized services are not very popular and are not interrelated with many elements of

the service system. They are often oriented for a specific audience for example, the

Flocktracker, offering a tool for specialists in city planning to collect data. These can build

extensions for the service systems.

The development of Digital Mobility Services, Services for navigation of cars will be taken by

vehicle technologies (sensors and interfaces) more than by Apps. Apps will focus on services

closer to human behavior (walking, Public Transport, Activity planning). Every App is awesome

from the view of its creator, but there are stronger Apps needed.

Official Apps seem basic in comparison to private ones, but they have direct access to the

information and therefore they are more reliable. The App Bayern Info has all the datasets

belonging to the traffic authority of the state of Bavaria in Germany, nevertheless the interface

of it is not very user-friendly and that’s why users prefer to use Waze, the crowdsourcing based

traffic navigator. The traffic authority of the state of Bavaria is not willing to disclose their data

due to the data protection and data safety. The integration of data from public authorities, and

programming skills from motivated teams from the private sector would take the industry closer

to an optimum.

Due to the fragmentation of the services, their limited features and the concerns of many

institution (data owners) for the deployment of data. A platform where different services

systems for mobility, data analysis tools and data sets can be found and traded would boost

and increase the quality of the Apps in general as well as ease its development.

The main key resources of an App are the data and the capabilities of the developers. They

have to match to generate a successful service. But what often happens is that the raw data

can be too complex and hard to understand for the developers. Easy to use and analyze data

is often hard to find and use. These are the main barriers that generate weaker Apps than the

what was planned. Based on the response of the survey for a Walking app, a customer story

will illustrate the situation for the developers, for the users and for the data owners. Afterwards

it will be shown how the platform can deal with it.


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Explanation for the IT Developer

Example of customer story

Firstly, the idea either from

an IT developer,

entrepreneur or company

is to be developed. This

involves the creation of an

App, a data analysis tool

or a website. The concept

is defined, the final product

and its capabilities are

defined too.

For instance, an App for walking is to be developed.

Apart from the capabilities

of the IT developers,

external key resources are

need to build the solution,

for instance data and

modular services.

Services are needed such as an account manager, a user’s

GPS locator, A map, a route calculator and a navigator.

For walking comfort, the datasets needed would be noise levels

on street, sidewalks quality and location of stairs.

For walking security, the databases needed would be street

lighting, spatial distribution of crime, land uses and pedestrian

flows.

The developers find only

some key resources, not

all of them are available,

up to date or suit to the

solution to be built.

They found services in form of an API of a base map, the route

calculator, navigator and an API of a user’s GPS locator. They

did not find and will have to build the account manager.

The developers found only the following datasets: the air quality

measurements and analysis, sidewalk quality, stairs location,

street lighting, spatial distribution of crime and land uses. No

Noise levels on streets or pedestrian flows exist for this city.

They inquire to the

institutions to access to

the data sets. The

developer should contact

many different

stakeholders in different

forms and times.

The air quality measurements and analysis belong to the

environmental authority. The sidewalk quality, stairs location

and street lighting belong to the construction authority of each

municipality of the city. The Crime distribution dataset to the

federal police. The land uses to the trade chamber. The map

and the route calculator are open source. The navigator and the

GPS locator belongs to the private company.

They are granted to

access to some datasets

and services, not to all of

them and sometimes

under special conditions.

They got access to the air quality measurements and analysis

in real time. They can have the dataset of sidewalk quality but

only for one measurement taken once last year. They can

access to the stairs location and street lighting but have to wait

two months after inquiring for it. The land uses dataset has

expensive costs. The crime distribution is confidential

information and cannot be accessed. The map is available for

free. The user’s GPS can be used only if this provider can store

the data generated by the users.


84

From all these data sets,

the developer can analyze

and understand only a

restricted amount of them.

They can only understand the air quality analysis and the stairs

positions. They also agree to pay for the land uses’ data set.

The stairs locations are in many different plains, impossible to

be built in the solution. He uses the map and the user’s GPS

locator.

The data that can be built

on the App is limiting the

services it was intended to

provide.

They can only build an App for walking providing comfort based

on air quality and stairs as well as security based on land uses.

The map works well but the user’s GPS locator makes the

solution non anonymous anymore.

The planned solution has been shrink, and the quality of the

planned services has decreased.

Figure 55: Schematic comparison of digital mobility solution planned vs. developed.


85

The data sets and services availability is limiting the capabilities of the developers. Moreover,

in the future the IT-developers will have to check for the datasets updates with each of the

different institutions. The solutions the IT developers can provide have a potential bigger than

the one seen currently on the market.

This concept of architecture for the platform will allow the open access to modular services

and data sets to develop solutions in the smart cities. IT developers will have a common place

to find data sets, data services and end products to develop more solutions and deploy them

there. The data collected by the authorities with sensors in the infrastructure will be used more

frequently generating more services and conveniences in the city. Furthermore, data providers

can get a share of the profits of the solution developed as well as the authors of the platform.

A new governance concept will have to be developed to regulate the shares of benefits, privacy

of usage and data as well as data sets storage and transfer. It will ease the IT-developers to

produce the planned services in a regulated and standardized way.

In the users’ perspective, a customer story is presented. They only expect to have a service

that fulfills their needs and preferences. These are in the best of the cases addressing the

goals for mobility analyzed in the previous chapter, but often there are other conditions

involved. Nowadays, users get what is developed in the market and it is limited by the key

resources available to the programmers creating them. A user needs to combine several

different of these single services to get the required solution. For instance, a user has an

appointment at a crowded place in a city. As he wants to take his own car, he uses many

different solutions for finding the optimal route, checking whether there is a traffic jam and

where he could park his car. Although he is only interested in arriving at his appointment in

time, he has to use many different single services.

The customers often have an “Expectation of free service” and want the services as cheap as

possible. After using the service, there are only two channels to provide feedback. One is by

how satisfied the customer is, rating the whole App with stars or entering in to a free text space

what they think of the App. The first one is narrow and unprecise, based only in one categorical

values for a complexity of features, the other is too open where the users input whatever they

would like to address. With the architecture platform of modular services, the users will have

more opportunities to get more services; furthermore, they will be able to give feedback to any

specific service module.

In the perspective of the data owners, the development of a solution is explained. The data

owners are often governmental authorities or major tech companies as google, INRIX or

TomTom. They collect and store the data by expensive devices. Then the IT developers

inquire them for the data. Since these are often start-ups or small businesses with limited

resources, they can rarely offer big compensations for the data. The process of accepting the

usage of the data can take a long time. Each organization have specific requirements for this.

The data owners stablish the conditions of data usage and often become a partner of the App.

A standardization of this process is needed. Depending on the deal they reach, the data will


86

be accessed only once, in real time or in the future. Either in raw format or aggregated. Either

complete or partial data sets.

Due to the current volatility of the conditions in the perspective of the IT developer, the user

and the data owner, the ease of the development of mobility solutions becomes a clear

necessity. To design an open platform for modular services would take the advantage of the

private sector developing Apps and the governmental sector providing the data. This platform

will offer modular services that can be integrated in Apps via open and standardized interfaces,

so called Application Programming Interfaces (API’s). This architecture allows the usage of

existing data in different services which can then be used for the development of innovative

mobility services. The platform is a key contribution and will bring together stakeholders that

offer digital services or plan to offer one.

This platform offers several modular mobility services with different levels of granularity. These

services should access different data sources and refine their information. Additionally, the

services should be hosted in a secure and safe environment. Many of these mobility services

are quite computation intensive, which leads to problems when there are too many service

calls. Finally, each user should be identifiable by the platform. The Open Mobility Services

Platform provides a direct connection between the resources and their demand, through a

legal framework, controlling who is accessing it and standardizing the resources provided.

The resources on the platform are standardized, this way similar datasets from different

sources can be presented and understood in a similar way. Moreover, the source can stablish

the requirements for the usage of the resource and get benefits from it, for instance in legal

terms or prices. For instance, the public transport data can be in different formats from city to

city. The platform will propose the public transport providers to use a standard format i.e. GTFS

(General Transit Feed Specification) or require to the sources to indicate in which format is

their data shown.

At the front end, the platform looks as a website. Users will provide their credentials and have

access to the different resources available in the form of API’s. This way, the API’s and the

data will be in a safe environment. A well-known example of this kind of websites is the

Framework for API’s “Swagger”. The proposed platform will create cooperation between the

public and the private sectors, they can create consortiums or Public Private Partnerships and

define who will operate the platform. Moreover, the cooperation with the governments ease

the compliance of the personal data protection laws (European Comission 2016).

Figure 1 shows the concept for the architecture of an open platform for modular mobility

services. It consists of the following elements:


87

Figure 56: Concept for the architecture of an open platform for modular mobility services.

Data sources

The platform is based on several different data sets. One data set could be floating car data,

which can be generated through on board units within cars or through smartphone Apps of

drivers. This data can be used for approximating the flow of traffic within the city and to detect

traffic congestion. It has been shown that it is sufficient to get data from only 2-3% of all cars

to provide accurate measurements of the velocity of the traffic flow (Herrera, Work et al. 2010).

Another relevant data source is parking lot data. This data could come from parking garages,

which already have a good knowledge about their available parking spaces. Data on on-street

parking spaces is harder to get. It could either come from sensors or from the crowd. For

instance, 8.622 parking spaces have been equipped with sensors in San Francisco (McNeal

2013). Several companies offer sensors for shriveling parking spaces (General Electric 2015,

Siemens Mobility 2015, SmartParking 2015). Other solutions are based on the crowd that

reports free parking spaces with an App (ParkMünchen 2015, Parkonaut 2015).

Additionally, data from public transportation providers and taxi corporations could also bet

relevant. They often already have own solutions that show time tables or the position of the

next available car. This data could also be a basis for the platform.

Layers of modular services

The modular services form the core of the platform. There are actually several layers where

the level of granularity increases from the top to the bottom. The services at the bottom focus

on analyzing and refining the data sources, whereas services on higher levels reside on

services from lower levels. The services on higher levels, integrate the services below, using

its results. The highest level takes theses integration and matches them with the inputs from

the user to deliver end user services.


88

Figure 57: Layer of modular services with example services.

Figure 2 illustrates these different levels and shows some example services. A simple modular

service could show the current traffic and parking situation on the streets in a certain area.

Another modular service could use this service, analyze the delivered information and predict

the parking situation for a specific point of time in the future.

The following services are examples for possible modular services:

Prediction of traffic situation: This service predicts the traffic situation for a certain

point of time in the future. It is based on the traffic situation service and on other data

sources such as weather data. This service processes the data with machine learning

algorithms.

Routing: The routing service calculates the best route between two points. The user

can specify whether the current traffic situation or the predicted traffic situation for a

certain point of time in the future should be considered.

Public transportation information: This service shows current and future timetables of

trains, subways and buses. It also provides information about any failures or

unforeseen situations.

Public transportation navigator: The public transportation navigator service suggests

the best public transportation route between two points for a certain point of time. It is

based on the public transportation information service.

Multimodal navigator: This service offers the optimal route within the city for car

drivers. It considers the traffic situation for selecting the optimal route, but also checks

where it is possible to find a parking space at the destination. Additionally, it checks

whether it is better to park the car near a bus station and to use public transportation.

This service is based on the previously described modular services.

Integration layer

The integration layer creates a secure and safe environment. The modular services can only

be accessed through the integration layer. The integration layer buffers service calls and acts

as a load balancer for the services. The user management and access control also reside in

this layer. As all service calls go through this layer, it can also be used for analysis of the

service calls.


89

Solutions

These are the solutions that users of the platform create. It is possible, that these solutions are

end user solutions or that the services are integrated into services outside of the platform.

One example for a possible solution is a scheduling and routing service for small and medium-

sized businesses that have multiple appointments within one or several cities. By considering

the routes between appointments and the predicted traffic situation at that point in time, the

scheduling of appointments can be optimized.

A solution already on development is the crowdsourcing logistics tool, ExCELL Transport. This

allows customers to request an order to transport packages. Couriers are companies that still

have capacity in their transport operations. These capacity available result in a competitive

disadvantage for small- and medium-size businesses. With ExCELL Transport, the couriers

can see the request as work orders, select them and schedule it in their agendas with the

routing included. ExCELL Transport is currently developed by TUM students under the

supervision of the chair for information systems. The development of this prototype used code

modules and data analysis API’s from the TUMitfahrer, explained in (Schreieck, Safetli et al.

2016).

Figure 59: Web App - Creating a new request

Figure 58: iOS App – Accept request and see routing

Conclusions

90

Conclusions

This work is one of the few found in the literature that make a deep review of the existing

mobility apps, and connects them to the users’ needs. In the last years, the richness of road

traffic data collection sources has grown substantially. Nowadays the data is related to a variety

of topics as such as occupancy, speed and traffic flow, and this variety is to be growing in the

coming years. The combination of traditional on-road sensors with floating car data techniques

can provide high quality traffic data in real-time that can be utilized by all the transportation

actors.

In 2008, Leduc (2008) foresaw the potential of smartphone generated data and predicted its

contribution to traditional ITS, however this has not happened yet. Due to the market size and

popularity of smartphones, this research proves the opposite, that smart infrastructure will

complement smartphone technologies. The main difficulty present and to be found in the future

is the data privacy issues. This is handled by data collection techniques, data randomization

methods or data governance.

Considering the services they provide, the Apps are supporting the cooperation of mobility

services. In the nearest future, public transport, car-share and bike share associations (that

work together) are to be seen more often. Furthermore, inter-modal cooperation organizations

are expected with the bike and car share cooperate together with public transport services.

Some of the Apps started already considering that mobility is larger from going from A to B,

and try to connect activities of the users. This is still a field in exploration with vast opportunities.

The Apps usage also can be used as a catalyst of the development of the transport network.

The more services through Apps are offered, the more developed the network is.

It was found that the idea of the car as an everyday transport mode is questioned towards a

more multimodal behaviour in a week scope, in which people use certain transport modes at

some days, and other modes in other times depending on their activities. Instead of the same

transport mode for all the days or different transport modes for the same trip. Furthermore, few

of the respondents associated walking with the usage of different transport modes. This can

be an explanation to the general overlook of the pedestrian infrastructure, regulations and

general attention.

The fact that none of the services reached a satisfaction level closer to low satisfaction than to

above expectations can be attributed to the feeling of play the “hard to get” of the people,

where they express being less satisfied than they really are. Another explanation can be that

they got used to the services after a long time using them and now they take them for granted

(Figure 49).

The services to be provided can have an infinity of solutions. This variety makes it hard to find

and chose a specific solution. Furthermore, the plurality of conditions in different cities or

countries makes it harder even to define the guidelines for an optimal solution. Therefore, to

provide very flexible platforms comes as a proper solution. It was found that there is also a

Conclusions

91

remarkable difference between what the App provider promises and what is really delivered.

For instance, the Bayern App info promotes they have a full Public Transport journey planner

using the data from smart infrastructure, while in the App it does only calculate a few train lines.

The need of cooperation between the private App developers and the governmental owners

of data becomes evident as well as Programmers with Transport scientists.

This work is subject to the following three limitations. Firstly, it implemented new methodologies

and theories from other fields in to transport science and it might sound uncommon for the

practitioners in this field, however, multi-disciplinarity is very positive.

Secondly, the survey was designed, applied and analyzed by a master’s student with less

experience with supervision from non-specialist in these techniques. Ask for only one open-

text-question to declare needs for more Mobility services instead of one question per service

system category. More research is needed for the prioritize of goals for mobility. In the survey,

during the tests it got discarded due to technical capabilities of the surveying platforms (Google

surveys, Survey Monkey, Lime survey). Due to the deployment of the survey through social

network, the most of the respondents might be in age between 20 and 40, located in Mexico

City or Germany, experiencing specific mobility conditions of their environment, minimizing the

degree of randomness of the sample. Socioeconomic variables were not measured.

Thirdly, the concept of the modular platform is still in the development stage. Its usefulness

and capabilities have been already proved by the ExCELL Transport Application. This is one

of the first attempts for the modularization of the mobility Apps industry.

For further research, a study like this can be applied to each category of service system, to

have an overview at a different level of granularity. A survey should be applied to assess the

quality of each service module and what are these missing. Also, it should be applied in a

specific region and the socioeconomic conditions should be asked to have more control over

the predictions.

One of the functions the ITS have is to regulate and assist authorities in enforcing the

application of the law. This does not happen with mobility apps, where some of them are either

at the limits of the law as the Apps warning users of the position of the hidden speed detectors

or warning the position of the police checks. Apps are on the side of the people in a less

regulated environment which is mostly controlled by private companies without a strong

governmental influence. This situation shows vast opportunities in the fields of governance.

Regulations can be enacted based on the information collected with the Smartphones,

however, this would decrease their popularity. Governments can use the strategy to exchange

rights in to governance of the data collected for the provision of their data. However, an

initiative like this should be carefully investigated.

The gathered results may indicate that the commuting time is shortening globally, after

comparing the Figure 46 with Figure 47 (Travel times globally vs Responses of the survey). It

has two possible explanations: either the world transportation systems are achieving shorter

Conclusions

92

commuting times than those in 1998, globally or the responses were influenced by random

conditions. In both cases, the situation illustrates how useful it is to work with a more advanced

technique to collect these data with better preciseness.

The market for intelligent mobility is growing intensively. The data collected by Smart

Infrastructure is not widely used. The mobility Apps provide high quality and deregulated

variety of services but struggle to get the data they need. The users expect an even larger

variety of services. The smart infrastructure is capable to collect and provide the data the Apps

needed. The Apps developers can provide the quality of services the users want. A Platform

offering modular services will convey the capabilities of both. This will enable the industry to

develop solutions closer to both, an individual and a global optimum.

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Pflügler, C., et al. (2016). "Predicting the Availability of Parking Spaces with Publicly Available Data." 2nd International Workshop on Big Data, Smart Data and Semantic Technologies (forthcoming), Klagenfurt, Austria.

Pflügler, C., et al. (2016). "A concept for the architecture of an open platform for modular mobility services in the smart city." International Scientific Conference on Mobility and Transport Transforming Urban Mobility, mobil.TUM 2016, 6-7 June 2016, Munich, Germany.

SAS (2016). "Univariate and Multivariate Models." STAT(R) 9.2 User's Guide, Second Edition Second Edition. Retrieved 5.04.2016, from https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_intromod_a0000000336.htm.

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https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_intromod_a0000000336.htm


http://www.naefrontiers.org/File.aspx?id=21923

http://www.amstat.org/sections/srms/pamphlet.pdf?q=error10

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http://www.mobility.siemens.com/mobility/global/de/nahverkehr/strassenverkehr/integrated-smart-parking-solution/seiten/integrated-smart-parking-solution.aspx


http://www.smartparking.com/

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https://www.surveymonkey.com/mp/sample-size-calculator/

http://www.un.org/sustainabledevelopment/sustainable-development-goals/

http://www.vamosportal.de/

https://www.weforum.org/agenda/2016/05/smartphones-are-closing-the-digital-divide-and-these-countries-have-made-the-most-progress?utm_content=buffer9cf5a&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer


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Zhao, Y. (2000). "Mobile Phone Location Determination and Its Impact on Intelligent Transportation Systems." IEEE Transactions on Intelligent Transportation Systems 1(1): 55-64.



List of Abbreviations

100


AADT Average Annual Daily Traffic

ANPR Automatic Number Plate Recognition

API Application Programming Interface

App Mobile Application

APTS Advanced Public Transportation Systems

ATIS Advanced Traveler Information Systems

ATMS Advanced Traffic Management Systems

CONACyT

Mexican Minister of Science and Technology, “Consejo Nacional de Ciencia y

Tecnología”

COUHES Committee on the Use of Humans as Experimental Subjects

CSS Customer Satisfaction Surveys

DAAD

German Agency of Academic Exchange. “Deutsche Akademische Austausch

Dienst“

DPM Direct Performance Measures

EUR € Euro (European currency)

FCD Floating Car Data

GTFS General Transit Feed Specification

HCM Highway Capacity Manual

iOS Mobile operating system created and developed by Apple Inc.

ITS Intelligent Transportation Systems

LOS Level of Services

MIT Massachusetts Institute of Technology

MSS Mystery Shopping Surveys

NGO Non-Governmental Organization


101

P+R Park and Ride

SDG Sustainable Development Goals

SFD Smartphone Floating Data

TUM Technische Universität München

UNAM National Autonomous University of Mexico

US $ Dollar (currency of the United States of America)

V2X Vehicle to infrastructure

VAMOS Verkehrs- Analyse-, -Management- und –Optimierungs-System

VKT Vehicle Kilometers Travelled

VSN Vehicular Sensing Networks

List of Figures

102

List of Figures Figure 1: Concept Matrix augmented with units of analysis (Webster and Watson 2002). .....................4

Figure 2: Scope of this research marked as the arrows ..........................................................................6

Figure 3: Growth of publications in the topics mobility and Smartphone Apps. .....................................7

Figure 4: Topic coverage of the literature review .....................................................................................8

Figure 5: Coverage of relevant topics per author ....................................................................................8

Figure 6: Coverage of topics per author aggregated ...............................................................................9

Figure 7: Boxplot measuring how many articles (16) address the target topics .................................. 14

Figure 8: Applications overview website. (Department of Transportation 2009) .................................. 17

Figure 9: Services provided by ITS. Adapted from: (Department of Transportation 2009) .................. 18

Figure 10: Parking information and routing display on a dynamic signal. Taken from (VAMOS Projekt

2012) ................................................................................................................................... 21

Figure 11: Dynamic signals to influence the traffic flow used in VAMOS Dresden (VAMOS Projekt

2012) ................................................................................................................................... 22

Figure 12: Dynamic signal - Prismenwendern changing from normal conditions to traffic jam. Taken

from (VAMOS Projekt 2012) ................................................................................................ 23

Figure 13: Display of traffic information. Taken from (VAMOS Projekt 2012) ...................................... 23

Figure 14: Type of data provided by different data collection technologies. Adapted from (Schmidt et

al., 2005) and (U.S. Department of Transportation, 2006) and (Peter Martin, 2003).......... 26

Figure 15: Data collected by different sensors Provided by (VAMOS Projekt 2012). .......................... 27

Figure 16: Costs of smart infrastructure sensors. (Department of Transportation 2009) ..................... 28

Figure 17: Interest over time of the concepts of: "Apps", Adapted from Google trends (2015) .......... 29

Figure 18: Gartner Hype cycle of emerging technology. Adapted from (O'Leary 2008). ..................... 29

Figure 19: Interest over time of the concepts of: "App transport”, "App navigation", "App traffic",

"App map". Adapted from Google trends (2015) ............................................................... 30

Figure 20: Categories of digital mobility services (Source: own analysis) ............................................ 33

Figure 21: Services modules in Digital mobility services systems ....................................................... 35

Figure 22: Data source popularity of usage .......................................................................................... 39

Figure 23: Data sources feeding digital mobility service systems ........................................................ 39

Figure 24: Data collection techniques with smartphones. (Catapult Transport Systems 2015) .......... 40

Figure 25: ten of the major sensors an average smartphone may be equipped with. (Matzen 2015) . 41

Figure 26: Goals mentioned by relevant sources ................................................................................. 42

Figure 27: Three -column strategy of Mobility / Technische Hochschule Ingolstadt (Wagner 2016) .. 44

Figure 28: Service quality loop of the European Committee for Standarization (2002) ....................... 46

Figure 29: Mentions of goals aggregated ............................................................................................. 49

Figure 30 Maslow’s hierarchical structure of customer needs for urban mobility ............................... 50

Figure 31: Organizational goals matching with the Apps roles ............................................................ 51

Figure 32: Box Plots explanation, adapted from Intechopen (2016) .................................................... 53

Figure 33: Statistical accuracy of a survey. Population size vs Sample size (as percentage of

population) Adapted from GreatBrook (2016) .................................................................... 54

Figure 34: Sample size calculation - z-scores ...................................................................................... 55

Figure 35: Type of model to be used according to the kind of data. Based on (Buechler 2007) and

(Bruin 2006). ........................................................................................................................ 56

Figure 36: Services needed for four stages of the trip ......................................................................... 59

List of Figures

103

Figure 37: Variables and values explanation......................................................................................... 61

Figure 38: Internet users per continent (Internet Live Stats 2016). ....................................................... 64

Figure 39: Estimation of population size ............................................................................................... 64

Figure 40: Progress of survey's collection from April 15th to May 15th 2016. ....................................... 65

Figure 41: Gender distribution of respondents ..................................................................................... 66

Figure 42: Smartphone savvy level reported ........................................................................................ 66

Figure 43: Boxplot of the reported commuting time ............................................................................. 66

Figure 44: Age distribution of the sample ............................................................................................. 66

Figure 45: Distribution of internet users worldwide as of November 2014, by age group (Statista 2014)

............................................................................................................................................ 66

Figure 46: Average daily travel time as a function of GDP per capita (Schäfer 1998) ......................... 67

Figure 47: Frequency of usage of transport mode. .............................................................................. 67

Figure 48: Frequency of usage of Smartphone based mobility service ............................................... 69

Figure 49: Satisfaction with smartphone based mobility services ....................................................... 70

Figure 50: Geographic Distribution of the responses ........................................................................... 71

Figure 51: Modular services as responses of declared preference. ..................................................... 75

Figure 52:Goals and Needs Addressed by Declared Responses from Users. ..................................... 77

Figure 53: Percentage of adults who reported smartphone ownership, and its change between 2013

and 2015 in selected countries (PEW Research Center 2015, World Economic Forum

2016). .................................................................................................................................. 78

Figure 54: Categories of services provided by ITS and Apps. Own elaboration with information from

Busch (2014) ....................................................................................................................... 79

Figure 55: Schematic comparison of digital mobility solution planned vs. developed. ....................... 83

Figure 56: Concept for the architecture of an open platform for modular mobility services. .............. 86

Figure 57: Layer of modular services with example services. .............................................................. 87

Figure 58: iOS App – Accept request and see routing ......................................................................... 88

Figure 59: Web App - Creating a new request ..................................................................................... 88

Appendix A: App Service Analysis

104


Nam

e

Serv

ice t

yp

e

Mark

et

sid

es

Ro

ute

pla

nn

ing

Real ti

me

navig

ati

on

Dyn

am

ic L

ocati

on

sh

ari

ng

locati

on

sh

ari

ng

Map

vie

w

Po

ints

of

Inte

rest

(PO

I)

Park

ing

info

rmati

on

Tra

ffic

info

rmati

on

Infr

astr

uctu

re

info

rmati

on

Matc

hin

g u

ser

to

user

Waze Trip planner 0

Moovit Trip planner 0

Google Maps Trip planner 0

Moovel Trip planner 0

Quixxit Trip planner 0

Drivy Car-sharing 2

BMW-Drive Now Car-sharing 1

Car2go Car-sharing 1

Bla bla car Ride-sharing 2

Lyft Ride-sharing 2

My Taxi Ride-sharing 2

Uber Ride-sharing 2

Gett Ride-sharing 2

Blacklane Ride-sharing 2 -

Flywheel Ride-sharing 2 -

Instantcab Ride-sharing 2

BMW – MyCityWay Others 2

Chargepoint Charging stations 1

BMW – ChargeNOW Charging stations 1

ParkN0w Parking service 2

Ampido Parking service 2

ParkPocket Parking service 2

Parknav Parking service 0

Zendrive Traveling analytics 2

Flinc Ride-sharing 1

MeinFernbus/Flixbus Trip planner 1

FleetBoard Traveling analytics 1

Allryder Trip planner 0

Öffi Trip planner 0

Blitzer.de Location-based information 0

HERE Maps Navigation 0

BVG Fahrinfo Trip planner 1

TomTom Blitzer Location-based information 0

Karten und Navigation GPS Navigation 0

HVV Trip planner 1

Blitzer POIbase Location-based information 0

FahrPlaner Trip planner 1

RMV Trip planner 1

Bus & Bahn Trip planner 1

easyGo Trip planner 1

München Navigator Trip planner 1

Stauinformationen Location-based information 0

Navmii Navigation 0

Citymapper Trip planner 0

ADAC Maps Navigation 1

Maps.ME Navigation 0

City Maps 2 Go Navigation 0

Urban Engines Trip planner 1

Car Jump Car-sharing 2

Nunav Navigation 0

nextbike Car-sharing 1

waymate Trip planner 1

Blitzer Radar Location-based information 0

Bayern Info Trip planner 0

Wunder Ride-sharing 2

General information Service components


105

Go

og

le

Oth

er

pri

vate

pro

vid

er

Pu

bli

c

tran

sp

ort

pro

vid

er

Pu

bli

c

ad

min

istr

ati

on

Sm

art

ph

on

e

sen

so

rs

Cro

wd

so

urc

ed

Wazehttps://www.waze.com/de/

https://www.waze.com/es/about/dev

Moovit http://moovitapp.com/#

http://blog-espion.fr/exemple-utilisation-open-data-moovit/

Google Mapshttps://www.google.de/maps?source=tldsi&hl=de

http://readwrite.com/2009/08/25/google_maps_gets_smarter_crowdsources_traffic_data

Moovelhttps://www.moovel.com/

https://www.moovel.com/en/ES

Quixxit https://www.qixxit.de/

Drivy https://www.drivy.de/

BMW-Drive Now https://de.drive-now.com/en/ http://www.bmwcarsharing.com/

Car2go https://www.car2go.com/

Bla bla car https://www.blablacar.es/

Lyft https://www.lyft.com/ http://www.lyftvsuber.com/

My Taxi https://us.mytaxi.com/index.html

Uber https://www.uber.com/

Gett http://gett.com

Blacklane - - - https://www.blacklane.com/ https://www.crunchbase.com/organization/blacklane

Flywheel - http://flywheel.com/ http://fortune.com/2015/04/30/flywheel-taxis-payment/

Instantcab https://www.instantcab.com/

BMW – MyCityWay https://www.cargomatic.com/, https://www.youtube.com/watch?v=vWV4MSQFulY

Chargepointhttp://www.chargepoint.com/mobile/ and

https://play.google.com/store/apps/details?id=com.coulombtech

BMW – ChargeNOW https://chargenow.com/

ParkN0w https://www.park-0w.com/

Ampido https://www.ampido.com/

ParkPocket http://parkpocket.com/

Parknav https://play.google.com/store/apps/details?id=com.faspark.android&hl=de

Zendrive https://www.zendrive.com/how-it-works/#none

Flinc https://flinc.org/

MeinFernbus/Flixbus http://parkpocket.com/

FleetBoard https://play.google.com/store/apps/details?id=com.faspark.android&hl=de

Allryder http://www.allryder.com/

Öffi https://play.google.com/store/apps/details?id=de.schildbach.oeffi, https://oeffi.schildbach.de/,

Blitzer.de https://play.google.com/store/apps/details?id=de.blitzer.plus

HERE Maps https://play.google.com/store/apps/details?id=com.here.app.maps

BVG Fahrinfo https://play.google.com/store/apps/details?id=de.eos.uptrade.android.fahrinfo.berlin

TomTom Blitzer https://www.youtube.com/watch?v=Ui8S4Hu0TLY,

https://play.google.com/store/apps/details?id=com.tomtom.speedcams.android.map

Karten und Navigation GPS https://www.youtube.com/watch?v=uG9eaeCWGbQ

HVV https://play.google.com/store/apps/details?id=de.eos.uptrade.android.fahrinfo.hamburg

Blitzer POIbase https://play.google.com/store/apps/details?id=de.navigating.poibase

FahrPlaner https://play.google.com/store/apps/details?id=de.hafas.android.vbn

RMV https://play.google.com/store/apps/details?id=com.cubic.cumo.android.rmv

Bus & Bahn https://play.google.com/store/apps/details?id=de.hafas.android.vbb

easyGo https://play.google.com/store/apps/details?id=de.easygo

München Navigator https://play.google.com/store/apps/details?id=de.hafas.android.sbm

Stauinformationen https://play.google.com/store/apps/details?id=de.knk.stauinformation

Navmii https://play.google.com/store/apps/details?id=com.navfree.android.OSM.ALL

Citymapper https://play.google.com/store/apps/details?id=com.citymapper.app.release

ADAC Maps https://play.google.com/store/apps/details?id=com.ptvag.android.adacmapformembers

Maps.ME https://play.google.com/store/apps/details?id=com.mapswithme.maps.pro

City Maps 2 Go https://play.google.com/store/apps/details?id=com.ulmon.android.citymaps2gofull&hl=de

Urban Engines https://www.youtube.com/watch?v=HFXjK67L058

Car Jumphttps://play.google.com/store/apps/details?id=com.ghm.carjump&hl=de

http://carjump.me/de/DE/home?orig=www.carjump.de/

Nunav https://play.google.com/store/apps/details?id=com.nunav.play&hl=de

nextbike https://play.google.com/store/apps/details?id=de.nextbike&hl=de

waymate https://www.waymate.de/

Blitzer Radar https://play.google.com/store/apps/details?id=com.lelic.speedcam&hl=de

Bayern Info http://www.bayerninfo.de/bi-app

Wunder https://play.google.com/store/apps/details?id=org.wundercar.android&hl=de

Data source

ReferencesName

Appendix B: Survey Templates

106


English version

Mobility Apps

The survey identifies the users' preferences of mobility services through Smartphone

applications. It is a part of the thesis of Gabriel Hernandez, mandatory to get the Masters

degree in Transportation Systems by the Technische Universität München.

Your participation is voluntary, anonymous and can be finished at any time without

consequences. After being analyzed and presented, the answers´ database will be destroyed.

More information (and an awesome dynamic map of the answers):

https://gabrielhernandezvaldivia.wordpress.com/transport/mobility-apps-users-needs-data-

requirements/

Your input is highly valuable to provide better services. Thanks :).

Sincerely ,

Gabriel Hernandez,

In cooperation with:

TUM Mobility Services Lab: mobility-services.in.tum.de

TUM Institute for Intelligent Transportation Systems

https://www.vt.bgu.tum.de/en/research/groups/

1. In which city or village do you live?

2. How long is your daily commuting time in minutes? (one way transport to your daily

activities)

2a. How familiar to Smartphone technologies do you consider yourself?

1 2 3 4 5



107

1. I dont use 2. Sometimes few Apps

3. Daily few Apps 4. Daily many

Apps 5. Progrmamer / Developer

Programmer.

3. How often do you use the following transport modes?

Never Rarely -

yearly-

Occasionally

-monthly-

Frequently -

weekly-

Very

frequently -

daily-

Walk

Bike

Public

Transport

Car

Taxi

Car-share

Ride-

share

Motorcycle

5. How often do you use smartphone for mobility services?:

Never Rarely -

yearly-

Occasionally

-monthly-

Frequently -

weekly-

Very

frequently -

daily-

Planning

before the trip

( which

stations, line,

transfers,

costs, etc) -

e.g. MVG,


108

Never Rarely -

yearly-

Occasionally

-monthly-

Frequently -

weekly-

Very

frequently -

daily-

BVG, Google

maps -

Navigation (

real-time

directions) -

e.g. Google

Maps,

Tomtom,

Waze -

Purchase

tickets for

transport -

e.g. MVG,

Deutsche

Bahn, Moovel

-

Charging

stations info (

for gasoline

or electric) -

e.g.

Chargepoint,

Tanktaler,

BMW Charge

now-

Car parking

assistance

(location,

reservation,

prediction,

etc) - e.g.

Parkpocket,

Parknav,

Parknow -


109

Never Rarely -

yearly-

Occasionally

-monthly-

Frequently -

weekly-

Very

frequently -

daily-

Ride, car-, or

bike-Share -

e.g.

Carpooling,

Blablacar,

Nextbike,

Car2Go,

Ecobici,

Blablacar -.

Information

for transport (

traffic,

infrastructure,

locations, etc)

- e.g. Waze,

Blitzer, Stau

info -

Taxi-hailing -

e.g. Uber,

Lyft, MyTaxi -

6. How these mobility services address your expectations?

I do not

know do not fulfill fulfill a bit fulfilled

above

expectations

Planning

before the trip

(which line,

stations,

transfers,

costs, etc) -

e.g. MVG,

BVG, Google

maps -


110

I do not


above

expectations

Navigation (

real-time

directions) -

e.g. Google

Maps,

tomtom,

Waze -

Buy tickets

for transport -

e.g. MVG,

Deutsche

Bahn, Moovel

-

For charging

stations ( for

gasoline or

electric) - e.g.

Chargepoint,

Tanktaler,

BMW Charge

now-

Car parking

assistance

(location,

reservation,

prediction,

etc) - e.g.

Parkpocket,

Parknav,

Parknow -

Ride-, car-, or

bike-Share -

e.g.

Blablacar,

Carpooling,


111

I do not


above

expectations

Nextbike,

Car2Go,

Ecobici,

Blablacar -.

Enter or get

information

for transport (

locations,

infrastructure,

traffic, etc) -

e.g. Waze,

Blitzer, Stau

info -

Hire a taxi -

e.g. Uber,

Lyft, myTaxi -

7. Is there a mobility service through an App you would like to have and have not found

yet?

9. In which age group you are?

o Younger than 18

o 19 - 24

o 25-30

o 31 - 40

o 41- 60

o 61 or more

10. Which is your gender?


112

o Female

o Male

o Other:

More info:

https://gabrielhernandezvaldivia.wordpress.com/transport/mobility-apps-users-needs-data-

requirements/

Version in Spanish: http://bit.do/gabrielhv-thesis-2

Thank you! // Muchas gracias!!


[email protected], [email protected]

In cooperation with:

TUM Mobility Services Lab: mobility-services.in.tum.de

TUM Institute for Intelligent Transportation Systems


Error! No text of specified style in document.

Appendix C: Survey Website


114


115

Appendix D: Programming Code in R for Linear Regressions

116

Appendix D: Programming Code in R for Linear Regressions

#Clean/reset memories

rm(list=ls())

#set working directory

getwd()

setwd("C:/users/Gabriel/Google Drive/1-Transportation Systems 2013

GHV/SUBJECTS/Thesis GDrive/GaboThesis")

#set the data

data<-read.table("C:/users/Gabriel/Google Drive/1-Transportation Systems 2013

GHV/SUBJECTS/Thesis GDrive/GaboThesis/data/input/data_Merge_1.csv", sep=",", fill =

TRUE,header = TRUE )

#data <- read.table("input/test.csv", sep="\t") must be CSV!!!! carefful also sep="," means

commas!

#view Data:

#run linear regression

#Dependient Variable: 6b_f_nav = 6b_Frequency_Navigation

#Indep Variables:

##Then LR is saved in variable reg, for instance:

reg=lm(X6a_plan~Smartp+X3c_pubtra+X5a_plan,data=data)

reg_1a=lm(X5a_plan~Smartp+X2_comm+age+X3a_walk+X3b_bike+X3d_car+X3c_pubtra+

X3e_cshare+X3f_taxi+X3g_rsha+X3h_mbike,data=data)

summary(reg_1a)

#See Results:

summary(reg)

Appendix E: Results of Linear Regressions

117


Reg_1: To check which people group is more likely to use each service. The people groups

were defined by, smartphone usage, commuting time and Transport mode usage.

a. Service: Journey planner: Reg_1a

Call: lm(formula = X5a_plan ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -3.7468 -0.5490 0.1758 0.7813 2.1346 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1790773 0.3370600 -0.531 0.596 Smartp 0.5655547 0.0641892 8.811 < 2e-16 *** X2_comm 0.0009234 0.0021620 0.427 0.670 X3a_walk 0.0366868 0.0578974 0.634 0.527 X3b_bike 0.0724870 0.0411305 1.762 0.079 . X3d_car 0.0100924 0.0462439 0.218 0.827 X3c_pubtra 0.2189751 0.0541215 4.046 6.52e-05 *** X3e_cshare -0.0550128 0.0806510 -0.682 0.496 X3f_taxi -0.0155837 0.0590562 -0.264 0.792 X3g_rsha 0.0215203 0.0751882 0.286 0.775 X3h_mbike -0.1190209 0.0794651 -1.498 0.135 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.083 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2922,Adjusted R-squared: 0.2704 F-statistic: 13.38 on 10 and 324 DF, p-value: < 2.2e-16

b. Service: Navigation: Reg_1b

Call: lm(formula = X5b_nav ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -2.9767 -0.4947 0.1250 0.6744 1.9151 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.162e-01 3.154e-01 0.368 0.71285 Smartp 5.080e-01 6.006e-02 8.458 9.44e-16 *** X2_comm -2.132e-05 2.023e-03 -0.011 0.99160 X3a_walk -1.740e-02 5.418e-02 -0.321 0.74831 X3b_bike 7.306e-02 3.849e-02 1.898 0.05856 . X3d_car 1.322e-01 4.327e-02 3.055 0.00244 ** X3c_pubtra 1.056e-01 5.064e-02 2.086 0.03779 *


118

X3e_cshare -1.677e-02 7.547e-02 -0.222 0.82432 X3f_taxi 3.623e-02 5.526e-02 0.656 0.51248 X3g_rsha 4.236e-02 7.036e-02 0.602 0.54751 X3h_mbike -7.730e-02 7.436e-02 -1.040 0.29929 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.014 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2447,Adjusted R-squared: 0.2214 F-statistic: 10.5 on 10 and 324 DF, p-value: 2.14e-15

c. Service: Purchase of tickets: Reg_1c

Call: lm(formula = X5c_purch ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -2.0259 -0.9409 -0.2084 0.8118 3.1020 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0359070 0.3574086 -0.100 0.920037 Smartp 0.2364565 0.0680644 3.474 0.000582 *** X2_comm 0.0005574 0.0022925 0.243 0.808052 X3a_walk -0.0033640 0.0613927 -0.055 0.956336 X3b_bike 0.0681271 0.0436136 1.562 0.119250 X3d_car -0.0659370 0.0490357 -1.345 0.179671 X3c_pubtra 0.0374927 0.0573889 0.653 0.514020 X3e_cshare -0.0442999 0.0855200 -0.518 0.604807 X3f_taxi 0.2139977 0.0626215 3.417 0.000713 *** X3g_rsha 0.0640635 0.0797273 0.804 0.422256 X3h_mbike 0.0252852 0.0842625 0.300 0.764311 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.149 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1133,Adjusted R-squared: 0.08592 F-statistic: 4.139 on 10 and 324 DF, p-value: 2.134e-05

d. Service: Charging stations: Reg_1d

Call: lm(formula = X5d_charge ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -0.9424 -0.3941 -0.2498 -0.0542 3.3866 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1332806 0.2400590 -0.555 0.5791 Smartp 0.0943408 0.0457165 2.064 0.0399 * X2_comm 0.0001021 0.0015398 0.066 0.9472


119

X3a_walk -0.0098881 0.0412353 -0.240 0.8106 X3b_bike -0.0120732 0.0292937 -0.412 0.6805 X3d_car 0.0413100 0.0329356 1.254 0.2106 X3c_pubtra -0.0250051 0.0385461 -0.649 0.5170 X3e_cshare 0.0194941 0.0574408 0.339 0.7345 X3f_taxi 0.1229899 0.0420607 2.924 0.0037 ** X3g_rsha 0.0543880 0.0535501 1.016 0.3106 X3h_mbike 0.0684509 0.0565962 1.209 0.2274 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7714 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.07839,Adjusted R-squared: 0.04995 F-statistic: 2.756 on 10 and 324 DF, p-value: 0.002828

e. Service: Parking assistance: Reg_1e

Call: lm(formula = X5e_park ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -0.8217 -0.2864 -0.1624 -0.0100 3.6783 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.228532 0.204550 1.117 0.264718 Smartp 0.015935 0.038954 0.409 0.682757 X2_comm -0.001333 0.001312 -1.016 0.310264 X3a_walk -0.056868 0.035136 -1.619 0.106527 X3b_bike 0.027755 0.024961 1.112 0.266988 X3d_car 0.036392 0.028064 1.297 0.195639 X3c_pubtra -0.027552 0.032844 -0.839 0.402157 X3e_cshare -0.007237 0.048944 -0.148 0.882543 X3f_taxi 0.068333 0.035839 1.907 0.057450 . X3g_rsha -0.006640 0.045629 -0.146 0.884385 X3h_mbike 0.183943 0.048225 3.814 0.000164 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6573 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.09223,Adjusted R-squared: 0.06422 F-statistic: 3.292 on 10 and 324 DF, p-value: 0.0004431

f. Service: Vehicle/ride share: Reg_1f

Call: lm(formula = X5f_share ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -1.8623 -0.6418 -0.1578 0.4207 3.6685 Coefficients:


120

Estimate Std. Error t value Pr(>|t|) (Intercept) -0.343657 0.280175 -1.227 0.220872 Smartp 0.120456 0.053356 2.258 0.024637 * X2_comm 0.001305 0.001797 0.726 0.468395 X3a_walk 0.015400 0.048126 0.320 0.749182 X3b_bike 0.259760 0.034189 7.598 3.26e-13 *** X3d_car -0.091557 0.038439 -2.382 0.017803 * X3c_pubtra -0.023922 0.044988 -0.532 0.595268 X3e_cshare -0.050126 0.067040 -0.748 0.455177 X3f_taxi 0.165403 0.049089 3.369 0.000844 *** X3g_rsha 0.235475 0.062499 3.768 0.000196 *** X3h_mbike -0.058181 0.066054 -0.881 0.379077 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9003 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2918,Adjusted R-squared: 0.27 F-statistic: 13.35 on 10 and 324 DF, p-value: < 2.2e-16

g. Service: Additional information: Reg_1g

Call: lm(formula = X5g_info ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data) Residuals: Min 1Q Median 3Q Max -2.31655 -1.14058 -0.00349 1.06621 3.08746 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1480305 0.4065348 -0.364 0.716000 Smartp 0.2236460 0.0774199 2.889 0.004128 ** X2_comm 0.0005507 0.0026076 0.211 0.832873 X3a_walk -0.0501491 0.0698312 -0.718 0.473184 X3b_bike 0.1210614 0.0496083 2.440 0.015209 * X3d_car 0.2059022 0.0557757 3.692 0.000261 *** X3c_pubtra 0.0709133 0.0652770 1.086 0.278134 X3e_cshare -0.2002794 0.0972748 -2.059 0.040303 * X3f_taxi 0.1822138 0.0712289 2.558 0.010978 * X3g_rsha -0.0092691 0.0906859 -0.102 0.918653 X3h_mbike -0.0002169 0.0958444 -0.002 0.998196 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.306 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1306,Adjusted R-squared: 0.1037 F-statistic: 4.866 on 10 and 324 DF, p-value: 1.503e-06

h. Service: Taxi hailing: Reg_1h

Call: lm(formula = X5h_taxi ~ Smartp + X2_comm + X3a_walk + X3b_bike + X3d_car + X3c_pubtra + X3e_cshare + X3f_taxi + X3g_rsha + X3h_mbike, data = data)


121

Residuals: Min 1Q Median 3Q Max -3.1907 -0.7286 -0.1161 0.6886 2.9193 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.112890 0.325710 -0.347 0.72912 Smartp 0.282600 0.062028 4.556 7.39e-06 *** X2_comm -0.002810 0.002089 -1.345 0.17956 X3a_walk -0.085913 0.055948 -1.536 0.12561 X3b_bike 0.021284 0.039745 0.536 0.59267 X3d_car -0.005014 0.044687 -0.112 0.91074 X3c_pubtra -0.077675 0.052299 -1.485 0.13846 X3e_cshare 0.134918 0.077935 1.731 0.08438 . X3f_taxi 0.516977 0.057068 9.059 < 2e-16 *** X3g_rsha 0.196765 0.072656 2.708 0.00712 ** X3h_mbike 0.026350 0.076789 0.343 0.73171 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.047 on 324 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.3377,Adjusted R-squared: 0.3173 F-statistic: 16.52 on 10 and 324 DF, p-value: < 2.2e-16

Appendix F: Goals addressed by declared service responses

122

7 Appendix F: Goals addressed by declared service responses

Physiological Safety Belonging /

LoveEsteem

Self-

actualization

Availability,

Accesibility,

Optimisation

of Mobility

Economic

development

, safety,

seccurity,

affordability

Social

Developmen

t, Customer

care

human well

beign,

Quality,

Comfort,

Ergonomy,

Innovation,

life-style,

sustainability

Bus information 4

Integrate a delay alarm 1

Integrate possible

delays and warnings1

Multi-modal and multi-

city for long distance

traveling

1

Multimodality 5

Preciseness 4

More cities 8

Integration of energy

consumption and

reliability

1

Frequency based

shortest paths1

Dyn

am

ic

Lo

catio

n

sh

arin

g

Preciseness

1

locatio

n

sh

arin

g

Dynamic locations of

public transits vehicles1

Bus information 2

Parking in critical

areas 2

Multimodality 3

Preciseness for

addresses 1

Proper cycle lane

indicators 1Po

ints

of

Inte

rest

(PO

I) Preciseness for

addresses 1Park

ing

info

rma

tion Parking information

3

Traffic flow in real time 1

Integrate with official

information of traffic 1Matc

hin

g u

ser

to u

ser

Cycle-share and Walk-

share / companions1

show routes of ride

share offers 1

Easier carpooling apps 1

all car sharing services

in one app 1

higher quality 2

Motorcycle share 1

Ch

arg

in

g

sta

tion

s

Gas stations with

additional services

(Restaurants, etc) 2Accidents and traffic

jams 1

Location based

probability to have an

accident 1

Location based

probability of crime 1

An

aly

zed

serv

ice

rela

ted

Response concept

Fre

qu

en

cy

Alre

ad

y e

xis

ts

Ro

ute

pla

nn

ing

Map

vie

wC

ar-/B

ike-, R

ide-

sh

arin

g

Lo

catio

n-b

ased

info

rmatio

n

Tra

ffic

info

rmati

on

Appendix F: Goals addressed by declared service responses

123

Tracker and route

predictor 1

Multimodal navigation 8

Bus information 1

Cycling navigation 3

Pu.T. Navigation 3

Speed

recommendations 1

Tra

velin

g

an

aly

tics

Freight transport

tracking 1

Walking Safe walking app3

Cycling app 3

Cycling app integrating

safety 1

Routes, Workshops,

Shops for cycling 1

Safe

ty

an

d

secu

rity

Integrate safety and

security in the

algorithm 5

Integrate weather

information 2

Connection with

emergency services 1

App versions for

disabled people 1

Multi-Lingual services

with local major

dialects/translations 1

Orientation for Rural

villages 1

Comparison of local

and international bus

and train costs 1

Illustrated train seating-

maps (with advises) -

similar as SeatGuru for

aircaft seating 1

cheapest flight tickets

without indication of

destination (Like in

Ryan air) 1

multi national and multi-

modal planning/

booking/ Paying/

Navigation app 1

To enter and generate

O/D information 2

Low carbon mobility 1

Autonomous car

hailing 2

Cable cars in the alps

Apps with low Battery

consumption 3

Wi-Fi in transport 1

Location more precise

than GPS 1

Inte

gra

tio

nA

ccessib

ilityH

ard

ware

Fu

ture

Pre

sen

tC

yclin

gN

avig

atio

n

Declaration concerning the Master’s Thesis / Bachelor’s Thesis

124

8 Declaration concerning the Master’s Thesis / Bachelor’s

Thesis

I hereby confirm that the presented thesis work has been done independently and

using only the sources and resources as are listed. This thesis has not previously been

submitted elsewhere for purposes of assessment.

Munich, July 10th, 2016

_________________________


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