Eindhoven University of Technology

MASTER

How to optimize train performance during delays with the use of intelligent automation

de Mooij, N.A.K.

Award date:2018

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HOW TO OPTIMIZE TRAIN PERFORMANCE DURING DELAYS WITH THE USE OF INTELLIGENT AUTOMATION

by N.A.K. (Nico) de Mooij

BSc Mechanical Engineering

Student identity number 0716343

In partial fulfillment of the requirements for the degree of

Master of Science

in Operations Management and Logistics

Eindhoven University of Technology

Master Thesis 1CM96

Final version

University supervisor:

Dr. L.P. (Luuk) Veelenturf, OPAC

Prof. Dr. E. (Eva) Demerouti, HPM

Company supervisor

dr. ir. A.A.M. (Alfons) Schaafsma, ProRail BV

TU/e. School of Industrial Engineering Series Master Theses Operations Management & Logistics

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Abstract HOW TO OPTIMIZE TRAIN PERFORMANCE DURING DELAYS

WITH THE USE OF INTELLIGENT AUTOMATION

By N.A.K. (Nico) de Mooij

The Netherlands has been using the current, manual, way of train operation for almost a hundred

years. Over the course of time, the system was slightly adjusted to keep up with the demand, but

now it is reaching its limit. People and companies keep asking for increase in reliability and

capacity, but there is not much room left for improvements. This requirement challenges the base

principles of train operation in the Netherlands and requires new, innovative solutions.

In this master thesis, the effect of automatic train operation on the Dutch railway network in

disrupted condition is evaluated and compared to the current way of working. By using a

microscopic simulation tool and different driving profiles, the effect of different levels of

acceptance and different levels of re-scheduling on punctuality, capacity and safety are

assessed.

With the use of these results and academic literature, recommendations regarding the benefits

and suggested implementation approach of automatic train operation are provided.

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Management summary

Motivation

Commissioned by ProRail, research has been conducted on the effects of intelligent automation

train operations in the Dutch heavy rail sector. The research was motivated by the lack of

sufficient information about the effect of employee acceptance of automatic train operations on

train operation performance on disturbed networks. This effect has not been well investigated

because automation has always been a difficult topic of discussion, despite the possibility of

significant benefits for train operation.

Objective and research questions

The objective of this research is to develop a better understanding of the effect of technological

acceptance of automatic train operation on train performance in a disturbed state, taking into

account different profiles for the train control system.

This objective is translated into the following main research question: “Under what conditions and

to what extent can train performance be optimized with the use of intelligent automation in

disturbed networks?'”

Theoretical background and views of automation

Automation has been a major point of discussion for decades. Generally, automation is perceived

as saving labor cost, improving quality and reliability, but also requiring more well-trained

personnel. According to academic literature, the real achievement of automation is the shift in the

role of the operator. This shift, however, leads to strikes and resistance till today.

One of the ways to improve the technological acceptance of automatic train operation is to

increase the perceived control employees have. To facilitate this process, train drivers should

be included in every stage of the design, implementation and evaluation. In addition, it is

important to provide products and applications that have benefits for both parties,

acknowledge drivers’ importance in the matter, organize additional pilots, listen to their

opinion and improve the solution together. To facilitate the acclamation, train drivers may

need support and should be given time to adjust to the new technology.

Though one of the most successful implementations of automatic rail operations followed these

theoretically defined rules, other implementations show that the way of introduction is not as

crucial as literature might indicate. In the Thameslink, for example, the organizers simply

demanded that every train driver use the automated system. As compensation, they received

more money. In case they did not comply, they were fired. Heretofore, no resistance has been

observed. There is also a third approach discussed in this study, which only partially follows

academic research. Instead of gradually familiarizing all train operators with Automatic Train

Operation (ATO), it starts with a new location, such as a port, where small groups of drivers are

trained and the network work expands continuously.

4

Simulation setup

To answer the research question, a microscopic simulation has to be executed. Nowadays, many

microscopic simulation tools are available. Within this research, three simulation tools will be

evaluated, namely: OpenTrack, RailSys and FRISO. All three tools can roughly execute the same

simulations and also will generate similar results. Based on workability, FRISO will be the

preferred tool for this research.

Since the Dutch railway network is too big to analyze, a simulation study of the biggest railway

bottleneck in the Netherlands is defined: The Schiphol Area.

To check the effect of acceptance and incorporating train data in the ATO algorithms on the

performance of automatic train operation, different driver profiles are defined: the sportive driver,

S-DAS based ATO with acceptance percentages of 25 to 100%, C-DAS based ATO and TMS

based ATO. The profiles differ from one another in acceptance-level and re-scheduling

algorithms.

In the simulation, all profiles are judged based on the spread in arrival time (capacity criteria), the

amount of unplanned red signal encounters / stops (safety criteria) and the percentile arrival

delays at Schiphol Airport and Hoofddorp (punctuality criteria).

Simulation results

Overall, the study shows that both the incorporation of re-scheduling methods and the

acceptance of ATO improve the trains’ performance on the predefined criteria. Given the results

of this study, the major benefits are in safety and capacity, while punctuality does not improve a

lot. In addition, one can say that a certain level of acceptance is required before significant

benefits can be witnessed. Furthermore, this study implies that the use of ATO for intercity trains

alone provides the same benefits as equipping both intercity trains and sprinters, which may be

due to the fact that most sprinters terminate in the simulation area.

The results found in this study align with the expectations given by academic literature as the

capacity and safety showed improvements with the use of ATO. In addition, the introduction of re-

scheduling increased the performance of automatic train operations significantly.

The importance of user acceptance has to be addressed as well. Academic literature clearly

shows that committed and motivated employees are essential for the success of a company. The

simulations run for this master thesis show similar results: without the acceptance of automatic

train operations, the benefits in safety and capacity are no longer significant.

Conclusion

The simulation showed that an increased acceptance of S-DAS based ATO improves the safety

of the train. To achieve this, an acceptance of at least 50% is required. To improve the capacity

by means of arrival spread reduction, at least 75% acceptance to outperform the base profile.

These results imply that the acceptance percentage has to be at least 50% to have significantly

improved performance, making train driver acceptance crucial for the performance of ATO.

From the theory, it was concluded that the technological acceptance is strongly connected to

engagement (perceived control, operator support and adapted recruitment), variety and pride.

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According to academic literature, all decision made during integration of ATO should incorporate

either of those elements to improve the technological acceptance of higher levels of automatic

train operations. Though this is the theoretically most supported approach, it misses two

important components: Cost and completion time. For this reason, another method is

recommended.

Before introducing ATO on the Dutch heavy rail network, the integration of a completely

autonomous transport system in a remote area is recommended. This enables you to safely

experiment in a closed area, while still being supported by a business case. In addition, the

amount of expected resistance is close to zero.

Simulation also showed that data incorporation has little advantage over predefined profiles in

case the train is not able to follow this profile by means of, for example, changing the sequence.

For this reason, I recommend to start with simple driver profiles and a basic traffic management

system without any data-exchange during operation outside of a train station.

After successfully realizing a fully automated area, this knowledge can be used to upgrade a

bottleneck area and gradually expand towards the main heavy rail network. After the network

matured, one can consider to significantly upgrading the network with continuous data-exchange,

re-scheduling rules and even train pods.

Recommendations

Overall, I recommend introducing automatic train operation on a small scale with simple driver

profiles. The preference goes to remote areas such as ports that already look into upgraded

freight transport solutions. Here, the introduction of S-DAS based ATO can be implemented as

real life example of the benefits of ATO without having any resistance from existing staff. Based

on this foundation, one can expand to less remote areas in the heavy rail network such as

bottlenecks. This method only encounters smaller group of train drivers, who can be supervised

and enabled without extremely high cost. This way, I expect them to feel less likely to resist as

they are low in numbers and honored with the first or second pilot of a new technology. In

addition, the initial cost of implementation is lower since rolling stock; tracks and the information

system are upgraded over time instead of all at once.

6

Acknowledgements

To my parents Ko and Lina, who raised me to be curious, motivated and open-minded, who have supported my development and who have loved me unconditionally. To my grandparents Adrie and Cor, who were always available for the additional support, no matter the situation. To my siblings Eric, Lia and Rob, who were always available for enriching discussions. To my girlfriend Marina, who supported me during the most difficult part of my studies with love and care. To my supervisors Luuk Veelenturf and Eva Demerouti, who who provided great insights, emphasizing support, valuable feedback and the motivation to be able to finish this project. To my supervisor at ProRail, Alfons Schaafsma, who provided the opportunities to take challenges in the railway branch, incorporated me in ATO related activities and supported me in gathering all required information. To my other colleagues from ProRail and the Netherlands Railways: Douwe, Joke, Wilco, Herman, Siebe, Marcel, Henri, Daniel, Ralph, Jan, Dolf, Ellen and many others, who enabled me to achieve my goals. To my old neighbor Edward, who has always been open for discussion or a random pizza. To Rob, Akemi and all the kendoka I met during national and international Kendo events and trainings. To Hoang, Nam and all interesting people I met during my internship at Doshisha University/Honda in Kyoto, Japan. To Larissa, Fabienne, Fabian and all amazing people I met thanks to my exchange semester at the ETH in Zuerich, Switzerland. To Natalia, Marcel, Mirjam and all others whom I met during my trips through Europe. To all of my amazing friends, who made my years as student an invaluable experience.

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Table of Contents

Abstract ............................................................................................................................................. 2

Management summary ..................................................................................................................... 3

Acknowledgements .......................................................................................................................... 6

Table of Contents ............................................................................................................................. 7

1. Introduction ............................................................................................................................... 9

1.1 Current situation .................................................................................................................... 9

1.2 Problem context .................................................................................................................. 10

2. Research Design .................................................................................................................... 11

2.1 Research assignment ......................................................................................................... 11

2.2 Research questions ............................................................................................................ 11

2.3 Scope of the research ......................................................................................................... 12

2.4 Research contribution ......................................................................................................... 12

2.5 Research methodology ....................................................................................................... 12

2.6 Structure of the report ......................................................................................................... 14

3. Technological acceptance of automatic train operations in the Netherlands......................... 15

3.1 Theory of automation and human involvement .................................................................. 15

3.2 Grades of automation in the railway industry ..................................................................... 15

3.3 The ironies of automation ................................................................................................... 16

3.4 Employee engagement and automation ............................................................................. 17

4. The effects of ATO on the tasks and joys of a train driver ..................................................... 19

4.1 Profile of the train driver ...................................................................................................... 19

4.2 Research: Factor Vijf .......................................................................................................... 19

4.3 The influence of different grades of automation ................................................................. 20

4.4 Expert opinion ..................................................................................................................... 22

5. Other views on train automation ............................................................................................. 23

5.1 AŽD Praha and SŽDC (Czech Republic) ........................................................................... 23

5.2 Thameslink Programme (United Kingdom) ........................................................................ 24

5.3 Deutsche Bahn .................................................................................................................... 27

5.4 Introduction of the RolTijdApp ............................................................................................ 28

5.5 Rail on demand ................................................................................................................... 29

6. Simulation structure ................................................................................................................ 30

6.1 Simulation input and output ................................................................................................ 30

6.2 Simulation environment ...................................................................................................... 32

6.3 Used data ............................................................................................................................ 36

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6.4 Assumptions and limitations ............................................................................................... 40

7. Study case: Schiphol Airport................................................................................................... 41

7.1 Problem definition of the case study ................................................................................... 41

7.2 Geographical boundaries ......................................................................................................... 42

7.3 Train lines and frequencies ...................................................................................................... 44

8. Simulation Results and Analysis ............................................................................................ 45

8.1 Data collection and adjustment ................................................................................................ 46

8.2 Matlab implementation ............................................................................................................. 47

8.3 Results: Validation .................................................................................................................... 47

8.4 Results: Safety ......................................................................................................................... 49

8.5 Results: Punctuality .................................................................................................................. 52

8.6 Results: Capacity ..................................................................................................................... 54

8.7 Case study evaluation .............................................................................................................. 57

9. Conclusions & recommendations ........................................................................................... 58

9.1 Main findings from the field research .................................................................................. 58

9.2 Main findings from study case ............................................................................................ 58

9.3 Feedback on the research questions ................................................................................. 59

9.4 Discussion and answer to the main research question ...................................................... 65

9.5 Suggestions for future research .......................................................................................... 66

Bibliography .................................................................................................................................... 68

Appendix A – Interview ................................................................................................................... 72

Appendix B – Thameslink London ................................................................................................. 74

Appendix C – Matlab file for data analysis ..................................................................................... 86

C.1 Data processing ................................................................................................................... 86

C.2 Safety criterion ..................................................................................................................... 98

C.3 Amount of runs ................................................................................................................... 104

Appendix D – Data logging ........................................................................................................... 106

D.1 OTT Logging ...................................................................................................................... 106

D.2 Safety logging .................................................................................................................... 107

Appendix E – TD plots for validation ............................................................................................ 109

Appendix F – TD plots .................................................................................................................. 111

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1. Introduction

Automatic Train Operation (ATO) is the key concept of this master thesis. Despite the low familiarity within the heavy rail, the use of ATO can dramatically improve train operations. In railway transport, safety enjoys the highest priority. Besides this, punctuality and reliability are a must to maintain customer satisfaction. Moreover, the railway sector contributes to the environment by providing sustainable solutions. Following the trends in the mass-transit rail, the use of ATO improves the safety, punctuality, reliability and sustainability.

This master thesis investigates the theoretical performance of ATO and compares it to the current way of operation in the re-scheduled situation. In addition, the perceived influence on train drivers is considered to strengthen the technological acceptance of ATO. The introduction elaborates on the current situation in Section 1.1 as well as the problem context, which is found in Section 1.2.

1.1 Current situation According to [1], automation is a widely discussed topic in different industries around the world. As stated in [2], the first airplane with autopilot was demonstrated in 1914, while [3] provides information about the first self-steering ships that successfully crossed the Atlantic Ocean in the 1930s. In addition, [4] reports that autonomous vehicles receive an increasing amount of positive attention, making their rise inevitable. Even within the railway industry, smaller systems are automated: unattended train operation has been the go-to way of operation within the mass transit rail for over a decade, with the first operational line starting service in 1981 as presented on the tourist information website of Kobe [5]. According to [6], extensive research was conducted to improve these systems over the years, while the heavy rail developments were at a standstill. Research by Albrecht et al. claims that perceived train driver resistance makes the main passenger railway operator in the Netherlands cautious with the consideration of ATO development [7]. Instead, they lean towards driver advisory systems which provide knowledge and instructions to the train drivers and are especially useful in case of disturbances or unforeseeable circumstances, as it helps the train driver remain focused on the safety of the train. The concept of driver advisory systems is a step in the right direction of automatic train operation, but requires operator acceptance to function. The conclusions presented by [8] show that demands, similar to the ones that led to the mass transit rail automation, now arise in the heavy rail sector. The interest in eco-driving, expanded capacity and improved reliability is increasing rapidly. The article also states that government interference and the raise in environmental awareness further establish the importance of these performance indicators. [9] explains that (partial) automation of train operations is required to satisfy these demands. Though there are undeniable differences between the mass transit rail and heavy rail, one cannot

deny the advantages of automation in the mass transit sector. ProRail believes that these

advantages can be transitioned towards heavy rail operations, and has started research

regarding the use of ATO on the main railway network. According to [10], evidence that proves

the advantages of ATO over the other ways of train operation (manual and manual with driver

advisory system) is required before a transition is considered.

10

1.2 Problem context The Netherlands has been using the current, manual, way of train operation for almost a hundred

years. Over the course of time, the system was slightly adjusted to keep up with the demand, but

now it is reaching its limit. People and companies keep asking for increase in reliability and

capacity, but there is not much room left for improvements.

The first major limitation is the inability to decrease the variation in driving attitude. As stated in

[11], every train driver has his or her own driving style. Some operators prefer to drive as fast as

the given speed limit at all times, while others try to save energy by means of coasting. Personal

behavior heavily influences the amount of time spent to go from A to B, as well as the

predictability of train location and speed. This subsequently leads to suboptimal capacity and

reliability.

The second major limitation of manual operation is human error. Even in case all train drivers

follow the instructions provided to them, some can have temporary emotional or health problems.

For this reason they might not be able to operate the train according to the predefined plan. Since

the Netherlands has one of the most crowded railway networks in the world, even seconds matter

when it comes to time allocations on railroad switches as explained in [12]. This problem

enhances the lack of predictability.

The third major limitation is the dependence on train drivers. Currently, operators are required to

relocate a train from A to B. Given the capacity demand shown in [13], more train drivers will be

needed to operate trains in the future. The sourcing pool of available train drivers, however,

decreases with time as the average age in the Netherlands increases, which can lead to train

driver shortages.

It can be concluded that the overall limiting factor is the human train driver operating the train. [8]

and [9] show that the introduction of (partially) automated train operations have the potential to

increase the throughput, capacity and reliability while decreasing the dependency on human

operators. This way (partially) automated systems will make it possible to meet the industry

demands.

However, up until today, no proper business case regarding the implementation of automatic train

operations has been defined, despite the perceived advantages it provides regarding reliability,

punctuality, safety and sustainability. Two of the causes of this problem are the resistance of train

drivers and the lack of simulation results that prove the benefits of ATO over other ways of

operation (manual with and without the use of driver advisory systems).

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2. Research Design

After numerous discussions, a scope was developed regarding the aim of the research. In

Section 2.1, the goal of this research is defined, while the research questions are formulated in

Section 2.2. Section 2.3 and 2.4 describe the scope of the project and the research contribution.

Subsequently, the used methodology is explained in Section 2.5, which serves as a guideline for

this project. The chapter concludes with an explanation of the report structure.

2.1 Research assignment The overall priority of ProRail is on-time and safe transport of passengers and cargo from A to B.

To achieve this goal, ProRail works on improving the safety, reliability, punctuality and

sustainability of the railroad network. The goal of the ATO project within ProRail BV and other

parties of the Dutch railway sector is to explore the advantages and disadvantages of automated

train operation compared to manual train operations, as well as the impact it has on the train

drivers and attendants.

According to academic literature, the use of ATO has different benefits over the use of manual

operation. Different case studies, such as the ones in [9] and [6], show these potential benefits,

but have mainly been focusing on the mass transit rail sector rather than the heavy rail sector.

Heavy rail generally refers to the intercity rail network built to be robust enough for heavy

passenger and freight trains. Characteristics of heavy rail include: heterogeneity

[freight/passenger, fast/slow], (inter)national traffic, and an open system [Railroad crossings,

open platforms]. Mass transit rail, on the other hand, is the type of high capacity transport

generally found in urban areas. Unlike heavy rail, mass transit rail is defined by homogeneity,

local/regional traffic, high operation frequency, and simple closed systems without railroad

crossings. Since ProRail mainly focusses on heavy rail infrastructure, additional research is

required to determine whether or not these benefits apply to the intercity rail network.

In addition to the benefits, another statement stands out from literature work, such as [14] and

[15]: The introduction of automated rail operation is challenging. Operators have never liked being

substituted by a machine.

Given the potential benefits and risks of automatic train operation in heavy rail industry, the goal

of this research is to extend the knowledge on what improvements can be achieved with the

introduction of ATO and which elements can facilitate the introduction in the heavy rail industry.

2.2 Research questions The goal of this master thesis is to provide more knowledge on the impact of ATO within the

heavy rail industry from both a technological and social standpoint.

The research question is defined as follows:

“Under what conditions and to what extent can train performance be optimized with the use of

intelligent automation in disturbed networks?'”

The main research question will be supported by several sub-questions to answer it as complete

as possible. To clarify the sub-questions, they are split up in two blocks.

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Technological acceptance

1.1 How do the findings regarding technological acceptance of ATO relate to the information

available in academic literature?

1.2 Given these findings, how to improve the technological acceptance of automatic train

operations?

Simulation

2.1 How can a simulation study investigate if the introduction of ATO has benefits for the heavy

rail sector?

2.2 Which simulation tool can be used for this simulation?

2.3 What is the effect of (partial) acceptance on the operational benefits of automatic train

operations?

2.4 Is there any operational benefit of incorporating the expected operational train data, such as

arrival time, in the planning strategy of automatic train operation?

2.3 Scope of the research The introduction of automatic train operation has been a major topic around the world. The

majority of the studies mainly focused on the mass transit rail. ProRail improves and maintains

the Dutch heavy rail network, which scopes this investigation on this network. In addition, the

disturbed situation sketched in the research question is limited to small deviations, currently

handled by the train driver, and delays/disruptions, currently handled by the dispatcher, as those

occur on a regular basis. Within the operational benefits of ATO, the focus will be on the capacity

of bottlenecks, safety and punctuality.

2.4 Research contribution This thesis aims to deliver a contribution to scientific knowledge, but adheres to the practical

relevance of the possibilities of automatic train operation. The Dutch railway network is

comparatively unique (just like almost all railway networks). Heavy rail train operation frequency

together and the large number of passengers during rush hour are the major reasons for its

uniqueness. For this reason, most other studies about the benefits of ATO are not applicable to

the railway networks of the Netherlands.

As this master thesis will focus on the heavy rail network and its operators in the Netherlands, the

unique design parameters for the Netherlands are taken into account. This includes, but is not

limited to, the ATB protection system, operating frequency and the high amount of railroad

switches. In addition, different simulation tools will be taken into consideration to determine the

results of the case study. The result of this thesis can be used as a stepping stone by ProRail in

order to implement the automation of train operation on the Dutch heavy rail network.

2.5 Research methodology The previous sections elaborated on the topic that will be researched. To satisfy the needs of the

different stake holders of this master thesis, a research methodology keeping the balance

between rigor and relevance is needed. Boeijen & Daalhuizen (2010) [16] provide such a

methodology and split the research into four different phases: Analysis – Synthesis – Simulation

and Evaluation – Conclusion. Figure 1 provides an overview of the different phases within this

thesis and shows the cohesion between the different phases. Subsequently, a subsection is

dedicated to the description of each phase.

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Figure 1 - Research structure

2.5.1 Analysis As the foundation of this thesis, a review of the literature was executed including different papers,

as well as reports from ProRail. Also, a review regarding the current state of automation and re-

scheduling [17] was used as a starting point for the information in this report. The following

subjects are analyzed within this phase:

Automation

Employee engagement

Automation in railway environments

Re-scheduling in railway networks

2.5.2 Synthesis In the synthesis phase, the structure of the simulation is defined. The structure is used to perform

the case study in the simulation step. During this phase, the following topics will be addressed:

Simulation input and output

Simulation software

Used data and assumptions

During simulation, one case study will be executed and will serve as a business case for

automatic train operations in the Netherlands. To determine this case study, the Dutch railway

network is analyzed. The railway network is used as is, since results can be compared to real

data and disturbances can be based on occurring disturbances.

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2.5.3 Simulation and evaluation In the simulation and evaluation phase, the case study will be simulated. Afterwards, the case

studys will be evaluated: Are they representative? The results will be compared to the results

determined by ProRail to judge the effectiveness of driver advisory systems. In addition, this

phase will evaluate the effectiveness of ATO over the conventional way of operation.

2.5.4 Conclusion and recommendations In the final phase, conclusions will be drawn based on the research done in the previous phases.

The main research question, as well as the sub-questions will be answered. Based on this,

conclusions, and recommendations will be written regarding further research and next steps

regarding ATO or ProRail.

2.6 Structure of the report This report is divided in nine different chapters. In Chapter 3, the theoretical background of

technological acceptance is discussed. This includes theory of automation and the difference

grades it has in railway operation. Subsequently, ironies of automation as well as employee

engagement are discussed. Chapter 4 discusses the effects of automation on the joy of a train

driver. By using the study results of a questionnaire executed by Factor 5 and expert opinions, an

evaluation is made. Chapter 5 elaborates on different views of automation. The perspectives from

Czech Republic, the Thameslink programme, Deutsche, and several Dutch experts are

evaluated. Chapter 6 focusses on the simulation structure and requirements. Topics include the

desired simulation output, the simulation environment and the used data to solve the study

executed for this research. Following the simulation environment, Chapter 7 elaborates on the

used study case and its limitations. Subsequently, Chapter 8 describes and evaluates the results

generated in the case study. Finally, Chapter 9 presents the conclusions of this research,

answers the research questions and provides recommendations for ProRail and further research.

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3. Technological acceptance of automatic train operations in the Netherlands

This master thesis focuses on the applicability and benefits of automatic train operations in heavy rail transport. The first chapters investigate the transition or integration phase and provide an answer to the first research question. Human behavior is a complex matter and requires significant resources to be understood. In automation, human factors are generally referred to as ‘considering the performance and well-being of human operators while designing jobs, machinery and the infrastructure these operators will use’.

According to [15], the transition to more advanced levels of automated systems introduces a certain amount of resistance in the work force, which will be addressed in Section 3.1. Section 3.2 follows with the grades of automation, commonly used in the railway industry. Section 3.3, subsequently, elaborates on the ironies found in automation. The chapter concludes with Section 3.4, in which relates automation and employee engagement.

3.1 Theory of automation and human involvement Looking through history, people have often confronted the introduction of (partial) automation with skepticism, especially when new technologies are first encountered. The Luddite movement at the start of the 19th century was the first major demonstration of concern for the potentially negative social impact of labor-saving machinery. To boycott automation, workmen attempted to prevent the use of knitting machinery in their industry by destroying them.

Since the second half of the 20th century, automation has made a lasting entry into the world of manual labor. As a result, many functions and operations, previously performed by human operators, have been taken over by automated devices. These developments resulted in increased productivity in terms of energy and material saving, improvement of quality, accuracy.

According to Sheridan and Verplank [1], the real achievement of automation is the shift of the role of the operator. Initially focused on manual control, now focused on supervisory control. Instead of performing simple tasks, like activating manual switches and following operation procedures, they perform intellectual and cognitive tasks of diagnosis, planning and problem solving. [18] states that with the use of automated systems, capabilities and advantages of both the human operator and the machine can be used to improve the performance. [19] shows that human-machine interaction and cooperation can be expressed by various levels of automation (LoA). Each of these levels defines a different degree to which a task is automated. This implies that automation is not all or nothing, but can vary across intermediate levels, with fully manual and fully autonomous conditions at the extremes.

3.2 Grades of automation in the railway industry Taxonomies are defined to fit within a certain scope. According to [20] and [21], a taxonomy

consisting of 5 grades of automation (GoA) is developed and standardized for railway transport.

Like most taxonomies, this approach is bound by the fully manual and fully automated levels. The

GoA definition is based on the tasks of the train driver and the conductor. For fully manual

16

operation (GoA0), these tasks consist of driving (accelerating/cruising/coasting/breaking), door

handling (opening/closing), intervening in case of disruptions, providing service to customers, and

ensuring safe operation. When a train is equipped with an automated protection system, its grade

of automation increases to GoA1. For GoA2, the train needs to be able to drive

(accelerating/cruising/coasting/breaking) without driver assistance, in addition to the automated

protection system. At this level, the train drivers' role changes from actively participating in the

control loop to monitoring the behavior of an automatic driving system and the traffic environment.

The train driver is still responsible for the safety in the train. In case of GoA3, train safety is no

longer the responsibility of the train driver. The remaining tasks are providing customer service,

handling the doors (opening/closing) and dealing with emergency situations. GoA4 is achieved

when the train is completely automated and drivers and conductors become obsolete. Table 1

describes the parameters applicable to specific GoA, while Table 2 provides clarifying examples.

Table 1 - Grades of automation in the railway industry: Definition

Type of train automation

Set train in motion

Stop train Door closure / opening

Operation in event of disruption

GoA0 Driver Driver Driver Driver Driver

GoA1 ATP with driver Driver Driver Driver Driver

GoA2 ATP and ATO with driver

Automatic Automatic Driver Driver

GoA3 Driverless Automatic Automatic Attendant Attendant

GoA4 Unattended train operation

Automatic Automatic Automatic Automatic

Table 2 - Grades of automation in the railway industry: Example

Example

GoA0 Manual operation without back-up: Tram driving through a city

GoA1 Manual operation with back-up: Train driver handles all actions while the protection system interferes in case something isn’t according to the rules.

GoA2 Driver sits in the cab, operates the doors and handles emergencies. Acceleration and braking happens automatically.

GoA3 Train attendant operates the doors and the train in case of unexpected events / emergencies.

GoA4 No on-train staff; the system is autonomous.

3.3 The ironies of automation Rapid developments have a significant influence on the way operations are executed these days. Automated operation saves labor cost, improves quality, accuracy, and reliability. However, Bibby et al. [22] point out that “even highly automated systems, such as electric power networks, need human operators for supervision, adjustment, maintenance, expansion and improvement. Therefore, one can draw the paradoxical conclusion that automated systems still are man-machine systems, for which both technical and human factors are important". This quote suggests that the increased interest in human factors among engineers reflects the irony that the more advanced a control system is, the more crucial the contribution of the human operator becomes.

According to [23], the designer of an automated system generally sees the human operator as unreliable and inefficient. This leads to two common ironies. First, the errors made by the

17

designer can be a major source of operating problems. Second, the designer tries to eliminate the operators from the automated system but leaves them the tasks he cannot automate.

More often than not, the task left after automation is monitoring whether or not the automatic system is operating correctly. In case of incorrect operation, the operator is expected to call a more experienced colleague or take-over himself. To take over in case of emergency requires a lot of experience and skill, since efficient, smooth, and swift handling is required. Unfortunately, unused physical skills deteriorate, which means that an experienced operator on monitoring duty might become inexperienced due to the lack of practice. [23] states that the most successful automated systems with rare need for manual intervention might need the greatest investment in human operator training. When the operator is expected to solely monitor the system and call the supervisor in case of irregular behavior, other problems arise. Vigilance studies done by [24] show that even highly motivated operators are only able to maintain effective visual attention towards a source of information on which very little happens for about 30 minutes. It is thus humanly impossible to carry out basic monitoring tasks for unlikely abnormalities, which therefore have to be executed by automatic alarm systems connected to visual and/or sound signals. The question remains - who or what monitors the automatic alarm system? Again, the operator will not monitor the automatic system effectively if it has been operating acceptably for a long period of time.

Besides the previously mentioned way of working, [23] elaborates on a system design based on

human-machine collaboration. In this case, instructions and advice are provided, the human error

is mitigated, work load is reduced or software-based displays are used to provide additional

information. Though all alternatives provide significant benefits, the human operator is no longer

obliged to fully understand the operation. Reduced intelligence, tunnel vision, and software

subjection are consequences that can lead to significant problems in case of emergency.

3.4 Employee engagement and automation Following the information provided in Section 3.3, finding a suitable employee is especially

important in case of (partially) automated systems: Tasks will become more important, but will

occur with a much lower frequency, which makes the work more monotonous.

Schaufeli and Bakker [25] define work engagement as “a positive, fulfilling, work-related state of

mind that is characterized by vigor, dedication, and absorption”. In this definition, vigor is

characterized by high levels of energy and mental resilience while working. Dedication refers to

being strongly involved in one’s work and experiencing a sense of significance and enthusiasm.

Absorption is characterized by being fully concentrated and happily engrossed in one’s work.

More information regarding these parameters is found in [26].

[27] showed that engaged employees are self-efficacious and optimistic people who exercise

influence over events that affect their lives. [28] adds that with a positive attitude and a high

activity level, engaged employees create their own positive feedback in terms of appreciation,

recognition and success. According to [29], engaged employees are often highly engaged in

other activities as well. Examples are sports, volunteering work or other hobbies. The engaged

employees, however, are not addicted to their work. Unlike workaholics, they work hard because

for them it is fun, while enjoying other things outside of work as well.

It is clear that engaged employees enhance a company’s performance. [30] states that they learn,

grow, display strong leadership skills, provide heightened return on investment and significantly

decrease the required amount of employee replacements.

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As mentioned in Section 3.3, a task will become more monotonous the moment it is (partly) automated. For this reason, it is important to carefully evaluate what challenges and aspects are removed or changed during the automation process. It can heavily influence the operators' perception of or the experience with the new technology. Especially in these situations, employee engagement is of most importance. Providing increased control to operators is one way to facilitate this engagement. To achieve this, operator involvement is key in every stage of the automation process. They are provided with additional opportunities to shape their workspace. In addition, operators gain a better understanding of what actually is going to happen, which reduces the amount of false perceptions. Both aspects improve the employee engagement and thus probability of successful automation. Perceived control can also be provided after the implementation of partly automated systems. Not obliging the use of automated functions during the introduction phase or giving the operator the ability to change to manual control at any time provides a certain degree of responsibility and power. This, again, enhances the employee engagement and thus their willingness to accept changes. Another point of consideration lies with the adaptation of recruitment requirements. According to [31], the railway transport branch does not yet select their operators with possible changes in mind, even though the likelihood of adaptations is rather high. Incorporating the possibility of automation in the selection process and providing correct information beforehand can significantly reduce the perceptive biases, and thus resistance, in case automation is realized. Finally, not just the operators, but also the management needs to be prepared for the change of

operation. Managers need a clear vision and direction before automated operations are

introduced. This way they are better able to provide the required job resources to their

employees, enhancing their engagement.

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4. The effects of ATO on the tasks and joys of a train driver

Despite the resistance against automation, most employees do not exactly know what changes

with the introduction of automatic train operation (ATO). To elaborate on this problem, this

chapter provides information about the effect of ATO on the tasks and joys of a train driver.

Section 4.1 introduces the tasks and desired job profile of a train driver, while Section 4.2

provides a summary of an investigation regarding train driver motivation executed by Factor Vijf.

Section 4.3 subsequently elaborates on how the earlier mentioned motivators and tasks will

change due to introduction of ATO. Given this information, Section 4.4 provides insights based

the experiences within ProRail and the Netherlands Railways.

4.1 Profile of the train driver Simply said, the train driver is an employee that operates a large mechanical device. He or she is responsible for the overall safety of the train. According to [32], the major tasks of the train driver consist of maintaining safety, the acceleration and deceleration of the train, as well as the signal handling. Additional tasks include checking the mechanical functioning of the machine before operation, informing passengers about unforeseen circumstances, taking security measures in case of disturbances and communicating with traffic controllers and dispatchers to make operation as smooth as possible. The safety of the passengers is not the responsibility of the train driver, but of the train chef.

To complete the prescribed tasks, the train operator has to be able focus for a long time and adapt to unexpected circumstances or situations. Though safety is the most important performance index, punctuality and environmental consciousness need to be maintained during operation whenever possible. According to different schools that facilitate train driver training ( [33], [34] and [35]), required qualities include a great sense of responsibility, being self-sufficient, stress-resistant and service oriented, as well as being able to enjoy the view.

4.2 Research: Factor Vijf In 2010, Factor Vijf did an investigation within the Netherlands Railways regarding the motivation in the career of a train driver. Nauta et al. [36] included 163 train drivers and former train drivers in their survey. Both groups were composed of different ages (<45, 45-55, >55) and completed a questionnaire. In addition, ten train drivers were interviewed. The research states that the majority of the train drivers (65%) believe they execute their work perfectly. Although this opinion is backed by good work performance, it introduces a couple of risks. Train drivers who perceive themselves as perfect executors of their tasks do not try to develop themselves and might get stuck in habits fairly easy. This behavior is not correlated to the train drivers’ age, but to how old they feel. Operators with a young mind are generally more eager to learn and less eager to oppose new technologies or changes in their job. The questionnaire shows that variety is the major motivator for train drivers. The youngest two

groups (<45 and 45-55) have it as most important motivator, while the employees of 55 and older

have it as second most important motivator. For the oldest group, job security took the first place.

Other important motivators are freedom, independent working, the sight, work-private life

balance, love for travelling, nice colleagues, salary, job security and doing a job that suits them.

The most important and least important aspects per age category are summarized in Table 3.

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Table 3 - The most and least important motivators for train drivers

Age category 45 years and younger 45-54 years 55 year and older

Most important motivators

Variety Variety Job security

A job that suits me Work-private life balance

Variety

Least important motivators

Career possibilities Career possibilities Career possibilities

Work pressure Work pressure Work pressure

The motivators are not the only important matter covered in the investigation by Factor Vijf. Nauta et al. includes the most common reasons for train drivers to search for another job as well. The most common reasons are the shifting working hours, accidents, health problems, the lack of a challenge, conflicts, job simplification and the availability of more pleasant or higher functions. With the introduction of higher levels of automation, it is important to not strengthen these motivators.

4.3 The influence of different grades of automation As seen in Section 3.2, the train driver tasks differ significantly depending on its grade. This section elaborates on the different aspects of train driver tasks, driver motivation and how they will theoretically change due to the introduction of higher grades of automation. Table 4 provides a summary of the effects of (partial) automation on the motivators provided by the investigation done by Factor Vijf and Table 5 gives a summary of the effects of automation on the reasons to quit being a train driver. Additional information regarding this matter is provided afterwards.

Table 4 - The effect of (partial) automation on motivational aspects of train drivers

Motivation aspect GoA2 GoA3-4

Variety Driving behavior restricted Completely different tasks

Freedom Driving behavior restricted Tasks more service-oriented

Independent working No change Possible cooperation with other train attendants

Love for traveling No change No change

The sight No change No longer available

Work balance No change No change

Nice colleagues No change No change

Salary No change Policy dependent

Job security No change People are still required but for other tasks

A job that suits me Stronger focus on train safety Completely different job profile required

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Table 5 - The effect of (partial) automation on reason to quit being a train driver

Reasons to quit GoA2 GoA3-4

Another nice job No change The change of tasks might encourage some to stay

Higher function No change No change

Working hours No change No change

Accidents No change Reduced

Health problems No change No change

Lack of challenges Less challenges (no more traction handling)

Completely different job and thus other challenges

Conflicts No change No change

Salary No change Depends on company policy

Train driver motivators As stated in Table 4, the introduction of ATO GoA2 only influences three out of the eleven most important motivators. When operating under ATO GoA2, the amount of required train drivers does not change. Each of them is still required and responsible for safe operation. Aside from traction handling, all remaining functions remain the same. This implies no changes in independent working, travelling experience, sight, work-private life balance, colleagues, salary and job security. Freedom, on the other hand, is restricted. Thanks to the auto-pilot, the train driver can no longer operate according to his favorite driving style. Variety is another influenced aspect, since challenges that involve traction, such as applying energy-efficient driving and comfortably putting the train at a standstill, are no longer under the train driver’s control. The last aspect influenced by the introduction of ATO GoA2 is the job suitability. The focus of the train driver moves even more towards operation safety rather than operating a large mechanical device. In contrast to GoA2, upgrading the system to ATO GoA3-4 yields a lot of additional changes regarding the motivational aspect of train drivers. The main reason for this change is the removal of the train driver as a consistently required position. His or her responsibilities regarding safety and train movement are executed by the ATO control unit. Other tasks, such as door closure and opening and operation in event of disruption or emergencies, are handled by a train attendant (ATO GoA3) or the automatic train controller (ATO GoA4). The only remaining position is the train attendant or conductor. A train attendant’s lust for travelling will be satisfied, while they keep nice colleagues and maintain the same work-private life balance. The sight, on the other hand, is no longer available. The train attendant will enjoy a lot of freedom and variety, but is no longer operating a machine. Instead, he or she provides service to the customer, with or without another train attendant. As a result independent working is no longer guaranteed. The demand for train drivers completely disappears under ATO GoA3-4. Though some train drivers might be willing to become train attendants, not everyone is suitable for the new type of tasks. The salary, as final motivator, completely depends on the company policies. The employee is no longer responsible for the trains’ safety, but receives more service-oriented tasks. At the moment no statement regarding the salary can be made in regard to the change of responsibilities. Motivators to quit Similar to the situation with the train driver motivators, ATO GoA2 only influences the minority of the defined motivators to quit as shown in Table 5. The availability of other nice jobs, growth possibilities, health problems and conflicts will not be affected by the upgrade to ATO GoA2. The same can be said for the working hours and salary, since the train driver keeps his or her responsibilities in the cabin. As the safety will improve [64], the amount of accidents, such as passing a red signal at danger, will decrease. The amount of collisions with suicidal people, however, will most likely not be affected as the current technology cannot enable the detection of people near the tracks before the operator can. In addition, the time a train needs to come to a standstill will not change either as it is mainly mass dependent. Depending on the train driver, it may or may not reduce the amount of people that quit due to accidents. The only reason,

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significantly influenced by the introduction of ATO GoA2, is the lack of challenges. By taking away the traction handling, the job is simplified which reduces the challenges of a train driver and thus increases the probability of someone looking for another job. With the introduction of ATO GoA3-4, not a lot changes in comparison with the introduction of ATO GoA2. The only change is visible in the severity of the effects. Under GoA3-4, most (or all) functions are automated, which means a train driver is no longer required. All train drivers that aren’t capable of transitioning or do not want to transition towards a job as a train attendant, have to look for another job. When considering the situation for a train attendant, other nice jobs, the ability to grow to higher functions, the working hours, the health problems and conflicts will not change. The salary changes will depend on the company policy, as explained in the previous subsection. Thanks to the automated system, the amount of accidents will most likely reduce. In addition, no-one will witness suicidal people jump in front of the train, making the consciously witnessed amount of accidents drop significantly. Whether the train attendant feels a lack of challenges cannot be defined, as it is unknown what their tasks will consist of.

4.4 Expert opinion This section is based on conversations with several experts within ProRail and the Netherlands

Railways and focuses on their views of, and experiences with, train drivers. Discussions and

conversations based on the other matter described in this chapter always emphasized the

following statement: train drivers will most likely not search for a job outside their field of

expertise. Generally speaking, Dutch train drivers are very proud and dedicated to their jobs.

They add that train operation is a specialized expertise. All these statements are confirmed by

Naute et al. [36].

Instead of moving towards other jobs, train drivers within the Netherlands Railways generally

strike to postpone proposed changes and keep the, for them, comfortable way of operation. In

addition to this statement, all the experts agree with the importance of employee engagement and

the need to be careful with their perceived control and responsibilities.

This implies several consequences for the introduction of higher grades of automatic train

operations. First of all, even small changes with regard to the motivational aspects of train drivers

can have a high impact on their engagement. In addition, train driver pride is something one

should not underestimate. It is of high importance that train drivers keep their perceived control of

the machine. Furthermore, it is important to introduce small steps with trials first. This way train

drivers can get comfortable with the new situation and provide feedback for further development.

This also improves their perceived control and impact. Finally, one can say that the motivators for

searching another job are insignificant in comparison to the earlier mentioned implications.

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5. Other views on train automation

In some other countries, the developments regarding ATO are in more advanced stages than in

the Netherlands. These experiences provide different views on the matter and might provide solid

stepping stones that smoothens the introduction of ATO in the Netherlands. This chapter

elaborates on these experiences and starts with the situation in Czech Republic, based on an

interview with employees of AZD Praha and the SZDC in Section 1. Section 2 continues with the

Thameslink programme, currently rolled out in the United Kingdom. Following the program in the

United Kingdom, Section 3 elaborates on the plans of Deutsche Bahn in Germany while Section 4

provides information regarding the first driver advisory systems in the Netherlands. Section 5

concludes the chapter with a more radical view on train operation.

5.1 AŽD Praha and SŽDC (Czech Republic) Take note that a large part of this subsection is based on an interview with employees of SŽDC and AŽD Praha. Správa Železniční Dopravní Cesty (SZDC), or Railway Infrastructure Administration in English, is the national railway infrastructure manager in the Czech Republic. AŽD Praha is an all-Czech producer and supplier of signaling, telecommunication, information and automation technologies. It is mainly focused on the rail and road transport field including telematics and other technologies. The questions and answers of the interview are added in Appendix A. According to [37], the first developments regarding ATO in Czech Republic started in 1960. The main goals were minimizing the energy cost of operation, better utilization of track and rolling stock parameters, as well as decreasing the train driver's workload. This subsection will elaborate on the business case, the integration process, and the social effects of ATO in the Czech Republic. The ATO business case According to the interview, the implementation of ATO was not supported by a strong business case. The main reason for implementation, despite the lack of a business case, was the reduced workload for train drivers. Especially during harsh weather conditions such as fog, rain and snow, the advantages were clearly noticeable. Another drive to implement ATO is the enhanced consistency and improved safety the ATO system provides. The train will depart exactly on time (not too late, not too early), and the train driver is able to focus on his primary task: observing the surroundings and guaranteeing the safety of the train. In addition, the train driver will not get confused by delays, since the train path is programmed beforehand. The implementation of ATO The implementation and integration of ATO in the Czech-Republic took a couple of decades. It started with a 200km track of which 60km were mounted with ATO GoA2. For two years, from 1991 until 1993, train drivers used these 60km to familiarize themselves with the ATO system. Since the train drivers need to be able to determine whether the ATO system behaves as desired, some old trains were equipped with ATO. This way the trains' behavior does not lead to any surprises, leaving the ATO system as only unknown parameter. During this familiarization time, the driver machine interface (DMI) is optimized by means of operator feedback. After the pilot, no further changes have been made to the DMI. From 1993 onwards more and more trains were equipped with ATO. Train drivers, however, were only able to use the ATO system on the designated 60km around Prague. The network on which it is possible to use the ATO system has been expanding since 2005. In January 2017, the total

24

amount of ATO equipped tracks was 1500km. The main reason for slow expansion is the lack of a solid business case. Despite the relatively low cost of implementation, financial resources were lacking since the implementation of ATO had a low priority. The social impact of ATO During the recruitment of train drivers, possible automation of train driver tasks is not taken into consideration. The older generation generally drives trains out of passion for the profession and trains, while the younger generation takes interest in the money they can make. Most young employees are no train enthusiasts and move to another employer the moment they are offered a higher salary. These employees stay in case no-one offers them a higher salary. The train drivers are trained in the use of ATO and receive a certificate after completing this training. This certificate serves as a permit for the use of train operation under ATO. When introduced at first, not everyone agreed with the upcoming automation. In general, however, there was not much resistance; most train drivers looked forward to the new technology. The main reasons for this attitude were the ability to choose whether ATO is used and maintaining the same salary no matter the preferred method of operation. The reason to not immediately oblige the use of ATO was completely psychological. The train drivers need to get used to new technologies before they can completely accept them. Another reason to not oblige it during the introduction included personal circumstances of the train drivers. Their mental state is important in the process of switching to a new method of operation. Despite the time required for familiarizing with the new system, most train drivers nowadays prefer the ATO system over manual driving. To maintain craftsmanship, train drivers are obliged to party operate the train manually. ATO reduces the workload of the train drivers. In rare cases of ATO failure, the drivers will enrage since they 'have to work'.

5.2 Thameslink Programme (United Kingdom) Take note that a large part of this subsection is based on the visit to the Thameslink in London on March 14, 2017. According to [38], the Thameslink Programme is a project in south-east England to upgrade and expand the Thameslink rail network to provide new and longer trains between a wider range of stations to the north and south of London without requiring passengers to change trains in London. The aim of these upgrades is to provide a service frequency of 24 trains per hour through Central London, offering a metro style operation on the busiest sections of the routes. [39] states that this kind of operation requires an inhuman degree of consistency. For this reason, ATO GoA 2 is pioneered on the central part, while the conventional operation is outside this crucial area. This section elaborates on the current situation and proposed solution, the safety and operation, as well as the social impact of the partial automation of train operations. The report about this visit can be found in Appendix B. Current situation and proposed solution In short, the goal of the Thameslink Programme is to realize metro headways on heavy rail infrastructure. The programme’s objective is to operate 24 trains per hour between Blackfriars and St. Pancras (the core), coming from different directions in the north and south. Figure 2 illustrates the desired situation and Figure 3 illustrates the current situation. The core is marked red in both figures.

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Figure 2 - Desired Thameslink situation (24 trains per hour)

Figure 3 - Current Thameslink situation (16 trains per hour)

Figure 4 - The critical railway switch (Blackfriars junction)

The core system consists of two tracks, where the distance between different trains is determined by signal locations and capacity. To improve the throughput, the signals are placed in the middle of the platform. The bottleneck of the Thameslink is located at the railroad switch south of Blackfriars: Blackfriars junction. Here, the trains to Elephant & Castle (southbound) cross the trains from London Bridge (Northbound). The location of the switch is pointed at with an arrow in Figure 2 and Figure 3. Figure 4 provides a more detailed overview of the bottleneck. Currently the maximal capacity is 16 trains per hour. As mentioned before; the goal of the Thameslink Programme is to achieve a capacity of 24 trains per hour over the core area. Network Rail showed with the use of simulation that the use of ATO

26

GoA2+ is mandatory in this situation. Without the use of ATO, the maximal achievable frequency is 20 trains per hour. Following the simulation results, two major reasons led to this result. First of all; the ATO system is way more consistent and predictable. This allows for the reduction of train scattering and leads to a frequency increase of 2 to 2.5 trains per hour. The second reason lies with the pre-described deceleration demands for train drivers in case of yellow and red signal

approaches. The maximal allowed deceleration for train drivers is 0.7 𝑚

𝑠2, while ATO is allowed to

brake with a deceleration of 0.9 𝑚

𝑠2, Despite being allowed to, a train driver generally does not

brake with a deceleration of 0.7 𝑚

𝑠2 , since it is rather uncomfortable. For comparison: the

comfortable braking deceleration in the United Kingdom is said to be 0.4 𝑚

𝑠2 . The operator

behaviour thus makes the difference between ATO and manual operation even bigger and leads to a frequency increase of 1.5 to 2 trains per hour. Implementation To accelerate the implementation of the Thameslink Programme, the Department for Transport (DfT) of the United Kingdom took several crucial decisions. The first decision was to merge all rail companies that use the Thameslink in their daily operation into one franchise. The second decision was the acquisition of a new train fleet, consisting of 115 Siemens class 700 trains that cannot be combined or split. The last decision was to obligate every company within the franchise to make use of these new trains in combination with the equipped ATO system. Every company that uses the new trains receives a bonus, while every company that refuses is not allowed to continue service after December 2018. These regulations assure a homogeneous fleet, one manager, one passenger railway operator and one control system, which is currently built. Due to the high train frequency within the core area, disturbances quickly develop into significant disruptions and obstructions. To compensate for this instability, storage sidings and reversing tracks are built outside the core area. Emergency platforms, additional tracks, and switches are added to facilitate these storage sidings and reversing tracks. When the problem is solved, a recovery scheme will restore the train balance between the north and south tracks. The recovery scheme includes an increased throughput of 30 trains per hour. In this case, dwell times are reduced and deceleration increased. Despite the theoretical capabilities of ATO, several tests regarding the protection system need to be executed prior to the transition. First, two digital tests were run through: one executed by the producer and one by the Network Rail testing and integration center. The third test is executed on ENIF, a test track similar to the core but on another location. The last tests were executed on the Thameslink itself, during night time. With the completion of this part, ATO equipped trains are able to automatically drive on the Thameslink core. During the publication of this report, the only remaining unknown factor is the Traffic Management System / Automatic Route Setting, which is currently in development by Hitachi.

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Analysis of the effect on the train driver Nowadays, more and more trains are substituted by the new 700 series. Though they are still operated manually under an automated protection system, train drivers can familiarize themselves with the new machines. This will not allow them to operate under ATO, since that requires specific training. The training is not yet planned, but train drivers already show different forms of resistance. To prevent strikes and everlasting negotiations with unions, several points were highlighted by the franchise.

1. With the new system, train drivers are better able to focus on their primary task: ensuring

safety.

2. ATO only runs on a small part of the total track.

3. In case of emergency, the train driver has to drive the train manually under the old

protection system.

4. Train drivers that use the new technology will receive a financial compensation.

5. The train driver is no longer responsible for the train movement within the core.

6. It will take several decades before higher grades of automation are introduced.

7. In case they lose their job, other jobs within the railway branch are available.

By this date (August 2018), no strikes with regard to automation have occurred. It thus seems that the statements helped to calm down the English train drivers. Whether this remains the same after implementation has to be seen.

5.3 Deutsche Bahn According to [40], Deutsche Bahn (DB), German Railways in English, wants to start experiments with trains without a single operator on board and claims to have the technology to do so since 1965. In the year 2000, DB decided they wanted to substitute all trains by driverless trains, starting with the ICE because it has its own, separate tracks. In the end, DB did not go through with it, because of the passenger experience: they are not used to driverless trains and might feel unsafe because of it. Due to this passenger perception, Deutsche Bahn altered its goals and decided to focus more heavily on the automation of freight transport. To compete with road and water transport, driverless trains are required. [40] states that DB Cargo and Siemens started the first project in 2015, with this goal in mind. Now, subsequent steps are planned. [41] explains that they aim to increase capacity, improve punctuality (5-10%), reduce energy consumption (10-30%) and cost. In addition, a new standard for security will be set, as human influence will be decreased and human failure - completely eliminated. During a conference in Lille, France, DB set a clear target for themselves: unattended train operation. This implies a design focus on GoA4, which has to be developed as an evolutionary model-based system engineering approach. The specifications for GoA2 will be derived from the GoA4 architecture by adding driver-related equipment. With the introduction of GoA4, Deutsche Bahn plans to increase the focus on passenger service, centralize the traffic management and control, automatically mitigate predefined incidents, and reinvent fully integrated railway systems in a European dimension. To achieve this goal, ATO should work independently of the protection system. In addition, vendor locks need to be prevented, while signal and obstacle detection have to be developed. The first major milestone for this project will be the pilot on the Gotthard Tunnel. The pilot has to demonstrate the technical possibilities and operational capacity of automated freight trains on the existing infrastructure. The main scenarios are energy-efficient ATO over ERTMS GoA2 tests on straight rails and automated shunting. More information regarding their vision can be found in [42].

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Despite their ambitious plans for autonomous train operation, the recruitment of train drivers within Deutsche Bahn continues. Within the train driver specification of Deutsche Bahn, no information regarding the use of ATO is included. The only requirement in the direction of ATO is the affinity with computers. This, however, is also required for current operation as the trains are already partly automated (e.g. protection system).

5.4 Introduction of the RolTijdApp The RolTijdApp is a smartphone application developed within the project EnergieZuinig Rijden, Energy Efficient Driving in English and EZR in short, and assists train drivers with a dynamic train shutdown advice such that the train arrives perfectly on time. The initiative shows gains in both energy consumption and arrival time deviation. This section elaborates on the introduction and implementation of the RolTijdApp test run, and is based on the report of the Roltijd Event held on March 21, 2017 [43]. The RolTijdApp was first introduced to ambassadors in 2014. At this time, the first ideas and concepts were discussed with focus on usability and simplicity. Following this evaluation, the application was introduced to a pre-selected group of train drivers. The pioneers used the RolTijdApp and were positively surprised by it. Following these results, the pilot was expanded towards a bigger group. Later in 2015, the pilot was expanded again and included train drivers that had no experience with EZR. The pilot showed the potential gains regarding energy consumption and reduced spread of arrival times. In addition, several points of improvement were listed. After the incorporation of the feedback, the RolTijdApp was spread among all train drivers of the Netherlands Railways. The implementation was followed by an evaluation event. The evaluation event focused on four main topics: safety, technology, communication and reduction of arrival deviation. For all topics, useful advice was gathered. [44] states that driver advisory systems might be considered a distraction to the train driver. The participants of the pilot, however, did not consider the RolTijdApp to be a distraction. Most train drivers were very happy with the application, but still missed some desired features. The application did not cover all available rolling stock and the GPS did not always function properly. Their approval was mainly focused on the simplicity and introduction of the application. Finally, train drivers perceived the application as a new challenge. Arriving perfectly on time (not too early, not too late) was the goal most testers had. Some train drivers even tried to beat the application to it. In the end, a lot was learned from the event and the test. During and after this pilot run, a lot of positive feedback was received. The strengths and weaknesses were evaluated, allowing for future development. In addition, a lot of admiration for the way of introduction was shown. The train drivers really felt in control of the process and received proper, personalized support during the introduction phase. They emphasized that this approach will be key for future implementation. Another remark worth remembering considers the driving profession: Train drivers say that this application really calls on their expertise, something they highly appreciate. Thanks to its success, the RolTijdApp will be adjusted according to the user feedback and added to the TimTim, a device aimed at aiding the train driver during operation.

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5.5 Rail on demand As explained in [45], the European Union aims to shift 50% of long-distance road freight and

medium-distance passenger trips to rail transport by 2050. One of the possible concepts that can

help realize this goal is “railway on demand”: Autonomous, agile and responsive railway pods

without a set schedule.

According to Veelenturf [45], being able to realize “railway on demand” would have huge benefits

in not having to wait for complete scheduled trains. Transport of passengers and freight could be

faster, more efficient, cheaper and in greater quantities. The vision accentuates the drawback of

the European rail system, which has been unable to keep up with the comparative cost and

flexibility of road transport.

Veelenturf, however, considers the cost of upgrading to an on-demand network to be

astronomical: the wagons need to operate with different voltages, wheel gauges and safety

systems. In addition, the railroad crossings need to be adjusted for increased traffic while a new

network manager is required for proper operation. Finally, every rail pod needs a small engine

and autonomous controller to function.

For the implementation, J. Spruyt [45] doesn’t perceive the cost nor the engineering as biggest

challenges, but believes that creating the right context and vision with different rail operators and

infrastructure managers introduces the biggest barriers. One way to slowly introducing and

experimenting with “railway on demand” is using small economic regions like the Port of Antwerp

or Salzburg. One can collect freight over day and dispatching this as combined long-haul cargo

daily. Spruyt states that future automation could allow the creation of 200 meter long self-

contained freighters, but doesn’t provide any timeline for an automated shunting yard debut. His

project’s most important outcome will be to spark discussions with authorities on the open

questions regarding automated rail.

Given the current state of rail and its plans, Veelenturf cautions that rail transport may be running

out of time to offer a replacement to road transport. Currently, freight trains are advertised as the

more sustainable option giving the CO2 emissions and traffic congestion of trucks. However, with

the rise and focus on electric, autonomous trucks that drive outside of peak traffic hours, this

argument might be obsolete in the coming 5 to 10 years.

Despite the potential, the rail industry still faces a real treat. Van Bers [45] states that rail without

‘rail on demand’ cannot compete with road transport, but in contrast to Spruyt, he thinks that the

biggest challenge lies in software that can instantaneously, automatically, and adaptively respond

to multiple simultaneous rail route alignment requests and thus control an entire network.

To (partly) facilitate the development of rail transport, the European Union and rail industry have a

game plan that includes many of the elements discussed in this section. Shift2Rail brings

together government and the entire rail industry to realize one goal: a single European railway

area that enables a mode switch to rail. Whether this works as planned has to be seen.

30

6. Simulation structure

In Chapters 3, 4 and 5, a literature review and several field studies were conducted to get a better

grasp on automation, the effect of automation on train drivers, and to determine how different

parties approach the introduction of automatic train operation. To get a better understanding of

the direct effect of the acceptance of automation and to answer the research question presented

in Chapter 2, microscopic simulation is required. During the microscopic simulations, several

different train driver profiles will be applied to the same case study with a sportive train driver as

base line. The results of the different simulations will be used to give answers to the research

questions. The case study is explained in Chapter 7, and the train driver profiles are explained in

Section 6.3.

This chapter will elaborate on the structure of the simulation. Section 6.1 focuses on the desire

simulation output, as well as the required input to perform a microscopic simulation for the

predefined case study. Section 6.2 elaborates on the available simulation tools and which one will

be used for the purpose of this master thesis. Subsequently, the Section 6.3 will explain what

data is used during the simulation. Section 6.4 concludes the chapter with an overview of the

assumptions and limitations for the study case in this master thesis.

6.1 Simulation input and output The simulations used in this master thesis are not the aim of the thesis and only serve to provide

answers to the research questions. For this reason it has to be clear what the desired output is

and what input is required to achieve it before the simulation can be executed.

Figure 5 shows different types of input required to run the simulations. Based on these inputs, the

simulation will generate different types of output. This section will provide a bottom-up approach

towards the simulation. First, the desired output is determined. Based on this output, the

corresponding input data is determined.

Figure 5 - Simulation process with input parameters, simulation and different types of output [45].

31

The bottom-up approach is used so that the amount of unnecessary simulation output is reduced

to a minimum. This saves time during the simulation process. For the input data, it has to be

determined which parameters are used and how these data are obtained. In addition, several

assumptions need to be made. These assumptions are described in Section 6.4.

Desired output Section 1.1 described several performance indicators whose importance increased over the last

decades. These performance indicators include capacity, safety, punctuality and eco-driving. To

answer the research question, these parameters have to be included in the output of the

simulation.

To evaluate the change in capacity, the deviation of the arrival time along all checking points of

the track is used. In addition to a graph that shows the expected timing at each position, a band

of two times the standard deviation of this arrival time is shown. The width of the confidence

intervals can then be used to determine how reliable the expected arrival time is compared to the

realized arrival time. When trains are more reliable (less deviation) in their arrival times, it is

possible to plan them closer to one-another, increasing the capacity of the system.

Due to the low number of accidents, safety in railway transportation is generally determined by

the amount of non-green signal stops and approaches as stated in [46]. Non-green signal stops

and approaches can be separated in three different parameters: stopped before red signal, start

braking for red signal and next signal red. The planned stops are not counted in any of these

variables.

In order to evaluate the punctuality, the absolute arrival lateness is determined and compared. In

addition, it is compared to the value three, since three minutes is the maximal allowed delay for a

train to still be on time. To visualize this parameter, the 10th till 90

th percentile of train arrivals are

plotted next to the expected time of arrival. Here the x-th percentile is the average arrival time of

the first x percent of the trains that arrived at a certain position. The slowest and fastest ten

percent of the trains are removed to prevent outliers as stated in [46].

Required input To create the desired output, specific input for the simulation software is required. Input

parameters are divided into two different groups: deterministic and stochastic input. The

deterministic input is used to run a deterministic simulation. In this type of simulation, all

parameters are defined by the user and do not contain any random components as explained in

[47]. This type of simulation is generally used to support timetable planning or the design of new

infrastructure. In a stochastic simulation, stochastic input is added in addition to the deterministic

input to add random components. These random components are necessary to represent

uncontrollable phenomena such as train delay or the time passengers take to board a train.

According to [47], this type of simulation is used to test the timetable robustness or to execute

stability analysis.

The base input for this simulation is deterministic as this mainly determines the simulation

environment. On top of that, stochastic parameters are introduced to simulate disturbances,

which allow us to test the different rescheduling methods. The relevant deterministic and

stochastic parameters are listed below.

32

Deterministic input The deterministic input contains the following parameters:

Infrastructure

This dataset contains all tracks required for the case study.

Timetable

This dataset contains the frequency and type of trains used in the case study. It also

contains the amount of stops of each train, stopping times at stations and timetable

variables.

Train driver profiles

This dataset consists of the different behavior and acceptance levels used in this

research.

Rolling stock

This dataset contains the rolling stock variables used for the simulation.

Stochastic input The stochastic input contains the following parameters:

Departure times

This data set contains the departure times of the different train types at the stations. The

arrival times can differ per simulation due to the behavior of the train driver. In addition,

the amount of passengers will affect the departure times.

Initial delays

This dataset contains initial delays of trains. By simulation, the effect of those trains on the

timetable will be measured.

6.2 Simulation environment Simulation software is required to calculate reliable results. Since a microscopic and deterministic

simulation has to be performed, the application needs to be able to execute such simulation.

Currently, there are a lot of different types of simulation software available. Within ProRail, there

are three available applications, which can execute deterministic and microscopic simulations:

RailSys, FRISO and OpenTrack. This section will elaborate on these three software tools and

explain which application will be used to execute the experiments.

RailSys One of the available microscopic simulation tools is the simulation tool RailSys. According to [48],

RailSys is developed by the German company Rail Management Consultants GmbH (RMCon)

and available since 1999. This application is used by over a hundred organizations worldwide.

Deutsche Bahn, Alstom and Bombadier are several more known organizations that use it.

33

[49] states that the infrastructure, timetable, rolling stock, operational data and dispatching rules

have to be inserted and configured to solve the case study defined for this master thesis. Figure 6

provides an overview of the workflow of the RailSys software. To simplify simulations, the

software output such as diagrams, train graphs, statistics and occupancy times can all be chosen

within the user interface.

The advantages and disadvantages of this tool are listed below:

Figure 6 - Workflow of the RailSys simulation software as found in [47].

Advantages

The software is standardized in the railway industry and therefore widely used. Results of

the case study can be compared with results from other countries.

ProRail is already using the system. Therefore, the current infrastructure and rolling stock

is already implemented in the system and easily accessible.

RailSys mainly focusses on planning. The simulation is directly executed after each

adjustment. This significantly reduces the time one needs to wait for the simulation to be

finished.

Disadvantages

ProRail only has a small amount of licenses for RailSys. Therefore, it is not possible to

use RailSys continuously and its use needs to be consulted with other employees.

ProRail does not have a license for the Dynamis module, which is required to adjust train

driver behavior.

34

FRISO The second available microscopic simulation tool is FRISO (Flexible Rail Infra Simulation of

Operations). FRISO is a discrete event-based microscopic simulator developed by ProRail and

used within the company for almost all microscopic simulations. This tool is based on a general

language called Enterprise Dynamics, which is used in a lot of industries. On top of the simulation

engine, ProRail constructed a library of railway components which can be used by the software.

According to [50], FRISO consists of different modelling elements that describe the different

concepts and functions representing railway practices. Figure 7 shows these modules.

Figure 7 - Modelling elements in FRISO as stated in [49]

In earlier studies executed with FRISO, trains [1] are running along the tracks [2], following the

predefined timetable [3] and speed limits. The operation may be disturbed [4] by delaying the

departure of trains, extending dwell times, and by varying acceleration and deceleration

parameters. The internal train control module [8] handles requesting and phased setting of

routes. The other parts have not been manually adjusted before.

To extend its possibilities, FRISO can be connected to external applications that take over parts

of its functionality. Any of the eight modules shown in Figure 7 can be taken over by an external

application. The current version has connections to a Traffic Management System (TMS) and to a

dispatching module that allows traffic controllers to interact with the simulation model. It provides

a man-machine interface for route setting tasks with a similar appearance as the systems used in

daily operation.

The advantages and disadvantages of FRISO are listed below:

Advantages

FRISO is developed by ProRail. For this reason, all knowledge regarding the software is

available within the organization.

It is used by ProRail for more researches, so any previous results can be used to

evaluate the results of this master thesis.

The simulation models used in FRISO are automatically generated with the use of Infra

Atlas, which saves a lot of time to implement a case study.

Train driver behavior is adjustable, which is crucial to answer the research question.

External applications can be used to take over crucial modules such as driver

characteristics and traffic control.

35

Disadvantages

FRISO is not used outside ProRail. Because of this, the results of the case study cannot

be compared to results outside of the Netherlands.

Since the software is not used in other organizations, the amount of reference material is

rather scarce.

The data provided by FRISO consists of running times, delay performance, headway

times and utilization but are not shown as graphs to the user. Therefore, additional data

processing is required to show the desired results.

OpenTrack The last available simulation tool that can be used for the simulation of the case studys is

OpenTrack. In the mid-1990s, OpenTrack started as a research project of the Swiss Federal

Institute of Technology (ETH) in Zurich. Since 2006, the development is taken over by the Swiss

company OpenTrack Railway Technology Ltd., a spin-off of the ETH Zurich. As stated in [51], the

software is widely used in the railway sector. A significant amount of well-known railway operators

such as Deutsche Bahn, SBB and SNCF use the software to analyze their network. In addition,

the software is used by consultants and research institutes such as Siemens, Alstom and TU

Delft.

As with the previously mentioned applications, the software has to be configured prior to case

study execution. The required infrastructure, rolling stock characteristics and timetable have to be

inserted. After the simulation is executed, the desired output data can be chosen in the form of

diagrams, train graphs, track occupancy times or statistics. Figure 8 visualizes the simulation

process of OpenTrack.

Figure 8 - OpenTrack simulation process as stated in [50].

36

The advantages and disadvantages of OpenTrack are:

Advantages

The software is standardized in the railway industry and therefore widely used. Results of

the case study can be compared with results from other countries.

Train driver behavior is adjustable, which is crucial to answer the research question.

Disadvantages

ProRail does not internally use OpenTrack. For this reason the amount of available

internal support will be limited.

ProRail does not have a business license for OpenTrack. A student license is required to

use this application.

The input data needs to be gathered from external parties, which requires additional time.

In case specific data are not available, they have to be manually added, which requires

even more time.

Software choice FRISO, RailSys and OpenTrack are all microscopic simulation tools, specialized in simulation of

railway operations. Each of these software applications allows the user to simulate train

movement according to accurate infrastructure models, the rolling stock, driver characteristics

and a predefined timetable and can theoretically be used to solve the challenges at hand.

Based on workability and feasibility, FRISO is the preferred tool for the simulation of the case

case study. The main advantage of FRISO lies in the availability of the software and all its

components. This saves time with the setup of the simulation software. Also, the current rail

infrastructure and rolling stock are available, as well as a traffic management system add-on.

Only the different driver profiles need to be manually added.

With this choice one, however, introduces additional work to post-process the data, since the

output of the application is not as developed as from the other two. Additional calculations and

post-processing are required to obtain a graphical representation of the data.

6.3 Used data This section will elaborate on the used data and which assumptions were made to execute the

case study. The data are categorized according to the input datasets.

Infrastructure The infrastructure dataset contains all infrastructure required for the case study and is

automatically taken from the InfraAtlas database, based on the boundaries defined in the case

study. This database contains the rail infrastructure of the Netherlands and includes stations,

track layouts (including block sections and signals) and track speed limits.

According to [52], track layouts are based on the NS'54 signaling system, while the speed limits

are regulated in accordance with Automatische TreinBeïnvloeding (ATB). Both are systems

mainly used in the Netherlands.

37

Timetable A timetable is required for any railway related simulation. For the case study used in this master

thesis, the time tables are taken from DONNA – the planning and capacity distribution system of

the Netherlands Railways. The obtained data includes:

Timetabling points

Platform tracks at stations

Stopping time at stations

Arrival time at timetable point

Departure time at timetable point

To make simulation variants, FRISO has a couple of edit functions to change the time table. This,

however, is not used within the scope of this research project.

Driver profiles The core of this research is the use of different driver profiles. For earlier simulations, ProRail

defined several profiles. These profiles include an agent and three automated driver profiles

based on different driver advisory rescheduling systems: S-DAS, C-DAS and TMS. Here, the

agent served as base line since it aims at modelling the behavior of the average train driver. The

agent, however, is still under development and cannot be combined with ATO based driving

profiles. For this reason a new profile is introduced as baseline: the sportive driver. Below is a list

of all used driver profiles and their characteristics.

For all automated systems, the optimal velocity is rounded per 5 km/h. The reason for rounding

lies in the most advanced algorithms, as it is their highest accuracy. For comparison, all other

profiles are adjusted accordingly. These automatic systems provide information to the train every

two seconds. The train’s location is provided to the system with the same frequency. All other

settings are kept the same if possible. Since simulation requires three different programs, some

contain the least common multiple.

Sportive train driver

The first added operating profile is the sportive train driver. As the name suggests, this operating

profile aims at reaching the destination in the fastest way possible. Ideally, this profile starts with

maximal acceleration, after which it operates at the maximal allowed speed. When close to its

destination, it decelerates with the regular service brake. Despite the urge for speed, the profile

respects the automatic protection system, as well as the requirements set by ARI. Given the

nature of this profile, trains operating under it reduce the amount of delay the fastest if the

signaling system allows. In addition, the punctuality is expected to be above average.

Agent

With the use of machine-based learning, the agent is a train operating profile based on real data.

According to [53], the variation in this profile is taken from probability distributions based on these

data. Since train drivers are people, not all train drivers operate the same. Some will try to

operate at maximal speed at any possible time, while others operate according to EZR principle

and coast whenever possible. For this reason, the agent combines a lot of different profiles into

one approximation. This results in an average train driver to which performance can be

compared. The profile always respects the ARI requirements, including the designated arrival

tracks.

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S-DAS based ATO

The first degree of automation is based on static driver advisory systems (S-DAS based ATO).

Given the operation information available at the start of the journey, the automated system

determines the optimal driving strategy such that the planned schedule is realized. The profile

accelerates at maximal acceleration, after which it operates at the optimal velocity such that the

train arrives perfectly on time. For deceleration, it applies the service brake. The optimal velocity

is never updated, no matter the changes in the environment (hence static). S-DAS based ATO

respects all requirements set by the automated train scheduler, ARI.

C-DAS based ATO

C-DAS based ATO is a more advanced ATO concept based on connected driver advisory

systems. Given the position of other trains, as well as the scheduled departure and arrival time,

the train speed is optimized every two seconds. The objective of this optimization is minimization

of the arrival and departure delays. In case the velocity is adjusted, maximal acceleration and

braking are used. The system always operates within the ARI boundaries. C-DAS based ATO

allows the train to approach red signals in case it reduces the delay, but aims to minimize

unnecessary stops. With this approach, trains will never deviate from their designated tracks or

take over other trains.

TMS based ATO

The full traffic management system or TMS based ATO is the most advanced ATO form tested in

this simulation study. Like the C-DAS based ATO, the TMS based ATO uses real time position

information as well as scheduled departure and arrival times to optimize the velocity of the train

every two seconds. The optimization aims at minimized delay at arrival and exit by minimizing the

amount of unnecessary stops. Like the C-DAS based ATO, it allows red signal approaches if this

reduces the train delay. As with all profiles, the speed update is rounded per 5km/h and track

changes are not allowed. The only addition in comparison to the C-DAS based ATO is the ability

to adjust the train sequence in case this is beneficial for the objective function. In case the

sequence is changed, the FCFS policy is used. In contrast to the other driver profiles, TMS based

ATO does not respects all requirements set by the automated train scheduler and can re-allocate

tracks while rescheduling.

Combined profiles

The combined profiles consist of S-DAS based ATO and the sportive driver. Both profiles are

used in accordance with a predefined binomial distribution. The set value for the chance of a train

following the S-DAS based profile equals 0.25, 0.50 or 0.75, depending on the profile. In addition,

two profiles are created in which either the intercity trains or the sprinter trains are equipped with

an automated controller.

The initial idea included the agent instead of the sportive driver but, since the agent is still in the

beta phase of development, this was impossible due to software limitations. Incorporation of C-

DAS or full TMS based ATO was not possible due to time constraints. The simulation software

was only available for a limited time and the incorporation of these profiles would require at least

500 hours of testing time due to the use of external algorithms.

39

The combined driver profiles consist of the following profiles:

25% S-DAS based ATO, 75% Agent

50% S-DAS based ATO, 50% Agent

75% S-DAS based ATO, 25% Agent

Intercity trains S-DAS based ATO, Sprinter trains Agent

Sprinter trains S-DAS based ATO, Intercity trains Agent

Rolling stock Another required dataset to execute a deterministic microscopic simulation is the rolling stock

data. Each rolling stock has its own parameters and therefore specific behavior. Running a

simulation with a different rolling stock type will create different simulation results and thus a

possible different outcome.

The most important train parameter for this simulation will be the traction effort of the train as this

parameter determines the maximal acceleration. Parameters like traction, friction and other

characteristics are taken from Lloyd's Register Rail Europe BV [54].

The type of rolling stock and length will vary given the time of the day. Since each rolling stock

type and length will create different results, the types used in the case study correspond with the

ones used in reality.

Departure delays and initial delays The disturbances used in the simulation are fit on real operation data of the Dutch train traffic

recorded between January 2016 and October 2016. The data are recorded on weekdays from

06:00h until 20:00h. To create a realistic and representative disturbance, the days with a

significantly (10%) lower punctuality than the average punctuality are removed. In addition,

outliers are removed as well. This includes trains with an absolute arrival delay larger than 20

minutes, as well as trains that depart at least 5 minutes early.

Due to restrictions in FRISO, trains that entered the simulated area with an absolute delay over 6

minutes are removed as well. Finally, [46] states the situation as described in the use-case is

most similar to morning rush hour traffic. For this reason, trains operating before 7:00 and after

11:00 are removed from the data set. A disturbance distribution is fit on the remaining data points.

Both the initial delays and the departure delays are based on this distribution. After picking a

value, the delay is adjusted to a maximum of 6 or a minimum of -6 minutes, since FRISO is

unable to cope with disturbances outside these bounds.

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6.4 Assumptions and limitations The following limitations are present for every driver profile:

The results of this simulation concern the potential benefits of ATO; not a direct effect of ATO.

Train speed and speed advices are rounded on 5km/h because it is the most accurate option

for C-DAS and TMS based ATO.

C-DAS and TMS based ATO communicate with traffic control every two seconds. This

applies to in- and outbound signals, as well as speed updates.

S-DAS based ATO has no in- or outbound signals and can adjust the speed at any given

moment, based on the predefined profile.

The train drivers react on advises regarding the train speed directly. The controller delay is

neglected because it will be in the order of milliseconds or smaller.

For S-DAS, C-DAS and TMS based ATO, Lloyd material characteristics are used for braking

and acceleration. C-DAS and TMS based ATO use these values in the predictions. In

practice, a train driver will not accelerate or decelerate as fast to enhance passenger comfort.

This implies that simulated trains spend less time accelerating and braking.

Simulation behavior is not dependent on timetable changes during rush hour or the evening

and models constant behavior instead to save time during implementation.

Disturbances are applied when a train enters the simulation or departs from a simulation

point. Trains will thus enter and depart on different times than indicated in the timetable.

For every train driver profile, the exact same disturbance (same value, not just distribution) is

applied to have more insight into the effect of each and every profile.

Height variations are not incorporated in the simulation as it has barely any influence on

passenger trains [46].

The departure procedure duration of the train equals zero. The main component of the

departure procedure is closing the doors. Because of the applied delay, based on real data,

the effect of this limitation is insignificant.

No FCFS principle is applied for the track assignment of Schiphol Airport station due to

software restrictions.

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7. Study case: Schiphol Airport

As it is too complicated and time-consuming to simulate the complete Dutch railway network with

ATO-controlled trains, a case study is used to determine the effect of ATO acceptance on train

operating performance. Due to the complexity of the Dutch railway network, a lot of different use-

cases are possible. Theoretically, most junctions can be used for the migration to ATO.

Given this information, the case study has to be chosen wisely. As stated in [39], (partly)

automating heavy rail operation may results in improved punctuality, capacity and safety. Since

those improvements are most beneficial in bottlenecks, only capacity bottlenecks are considered

for the determination of the research. From all the bottlenecks in the Dutch railway network, one

stands out most: Schiphol Airport.

This chapter elaborates on the case study and starts with the problem definition in Section 7.1.

Subsequently, Section 7.2 provides information about the geographical boundaries. The chapter

is concluded with the train lines and frequencies described in Section 7.3.

7.1 Problem definition of the case study According to [11], Schiphol area has been a point of discussion for years. The railway station of

Schiphol Airport is located underneath the entrance hall of the airport, which allows travelers to

enter and exit the airport by train easily. The track layout of the Schiphol Area is shown in Figure

9. Given the current rail infrastructure and the origin and destinations of the train services, the

number of conflicting situations between trains is expected to increase rapidly, which will

negatively affect the punctuality. Adding new tracks is extremely expensive due to the

underground location, while adjusting the timetable is not sufficient to handle the demanded

capacity.

Figure 9 – Infrastructure lay-out Schiphol area [52].

To tackle this problem, Middelkoop and Loeve [52] evaluated a combination of small changes to

the infrastructure and DTM-solutions (Dynamic Traffic Management). In their research they

evaluated several scenarios and determined that, without changes in infrastructure, one can only

operate the desired amount of trains in case the planned running times are extended with 3

minutes. In addition, they showed that changes in infrastructure have a positive effect on the

capacity, but are not enough to handle the desired amount of trains with the current planned

running times.

From this they concluded that Dynamic Traffic Management (DTM) is necessary. The most

effective scenario evaluated in this paper contains a First come First Served (FCFS) principle with

dynamic platform usage. To satisfy the desired punctuality of 87%, one additional minute of

running time is added to the timetable. The simulation results were very promising and lead to

42

real life test on the Schiphol area during the end of 2005. It was shown that the simulated control

strategies are feasible in practice as well.

Despite these solutions, [11] shows that the Schiphol area will soon face more capacity problems.

Tough problematic, the tunnel is not the only challenge in the railway network around Schiphol

Airport. Since Schiphol’s primary function is facilitating air traffic, many passengers carry large

amounts of luggage, which results in increased dwelling times and uncertainties in the passenger

flows. On top of that, Schiphol Airport is used as final destination for a significant amount of train

lines and serves as a major transfer hub for railway passengers. As with every termination, final

checks are executed to determine whether everyone left the train. This, again, additionally

increases dwell time and dwell time deviation.

Another challenge of the Schiphol area is the series of railroad switches when approaching

Hoofddorp. Here trains towards the rail yard delay trains from and towards Schiphol because of

lower velocities while using the same tracks. In addition, it leads to more yellow signals and more

cautious train drivers.

To tackle this bottleneck, the Netherlands Railways and ProRail have been discussing possible

solutions. Since expanding the amount of tracks in the Schiphol Area is still incredibly expensive,

possibilities regarding capacity are evaluated. As passengers are a factor that cannot be

controlled, other parts of the process have to be optimized.

7.2 Geographical boundaries Schiphol and Hoofddorp are the main bottlenecks of the Schiphol area. To incorporate network

effects, additional tracks and train stations leading up to bottleneck are added. On the east side,

the area is extended towards Amsterdam Zuid and Amsterdam Lelylaan. With this addition, all

arriving trains from the east depart from a train station within the simulation. On the west side,

train stations up to Leiden are included for the same purpose. For the high speed line, the next

possible train station would be Rotterdam. Since this is too far from the core area, several train-

path points between Rotterdam and Schiphol are added. This way, the trains still have simulation

and regulation time within the simulation, allowing for adaptations in case an ATO-based driver

profile is used.

The line from Amsterdam Zuid to Schiphol consists of four tracks. The line from Amsterdam

Lelylaan to Schiphol consists of only 2 tracks. Both eastern directions are combined at the

Riekerpolder aansluiting, where six tracks are combined into four tracks. Thanks to the flyovers,

this connection is not as much of a bottleneck as the other Schiphol station and the railroad

switches near Hoofddorp. When arriving at Schiphol, the four tracks diverge into six platforms.

Schiphol Airport uses the First Comes First Served policy towards arriving trains to smooth out

the arrival process. Between Hoofddorp and Schiphol airport, four tracks are available ending in

four platforms at Hoofddorp station. Before the train station, two additional tracks are available for

trains that do not stop at Hoofddorp. From Hoofddorp, two tracks go to the high speed line, two

go two Leiden and two go to the Hoofddorp train yard. These lines, however, are all connected to

one-another and include the station by-passes. The high speed line and the lines to Leiden are

not expanded in the simulation area. Figure 10 contains the graphical representation of this area,

while Figure 11 provides a more schematic overview.

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Figure 10 - Graphical representation of the simulated area around Schiphol Airport

Figure 1 - Simulated infrastructure of the Schiphol Area

Before the trains enter the Schiphol tunnel on the east side, they have a maximum allowed

velocity of 130km per hour. In the tunnel this limit increases to 140km per hour. After Hoofddorp,

this limit increases to 160km per hour. Trains on the high speed line have a maximal velocity of

300km per hour.

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7.3 Train lines and frequencies For the Schiphol area, the current timetable consists of intercity, sprinter and high speed services

(Thalys). The sprinter service stops at every station. The intercity service stops at Schiphol and

Leiden, while the high speed service only stops at Schiphol Airport.

Intercity trains approach from Amsterdam Zuid with a frequency of eight times per hour. All trains

terminate in Schiphol and are led to the rail yard near Hoofddorp. From Leiden, intercity trains

approach Schiphol with a frequency of four times per hour and leave the simulation via

Amsterdam Zuid. Regional trains arrive from Hoofddorp with a frequency of six per hour. From

these trains, two terminate and leave the simulation via Amsterdam Zuid while the other four

leave via Amsterdam Lelylaan. In addition, two regional trains arrive from Amsterdam Zuid and

terminate outside the simulation area in the direction of Leiden. Table 6 provides an overview of

these trains.

Table 6 – Current train lines and frequencies passing the Schiphol area.

Service type Start End Frequency

Intercity Amsterdam Zuid Railyard Hoofddorp 8x / hour

Intercity Track from Leiden Amsterdam Zuid 4x / hour

Sprinter Amsterdam Zuid Hoofddorp 2x / hour

Sprinter Amsterdam Lelylaan Hoofddorp 4x / hour

Sprinter Amsterdam Zuid Track direction Leiden 2x / hour

Thalys Enter via Amsterdam Zuid High speed line 1x / hour

All the trains listed in Table 6 operate in both directions.

45

8. Simulation Results and Analysis

The simulation of the case study is performed with the application FRISO, as explained in Section

6.3. First, the driver profiles designed by ProRail to determine the potential benefits of driver

advisory systems are used and compared to the results of the same profiles in this research. This

concerns the agent, the S-DAS based ATO, the C-DAS based ATO and the TMS based ATO.

These profiles serve as a reference case and are used to determine whether the implementation

was successful. To get the results for case study and answer the research questions, the

following steps were taken:

Insert infrastructure To run a simulation the infrastructure needed for the case study is inserted into FRISO. This task

is performed using the infrastructure manager of FRISO. As a base for the infrastructure, the

Dutch Infrastructure Network 2016 created in previous ProRail studies is used. This database is

made by ProRail and used for different capacity simulations within ProRail and the Netherlands

Railways. After checking the imported infrastructure for any defects, it was ready to use as no

defects or issues were found.

Insert rolling stock data The second preparation step before running the simulation is adding the rolling stock data in the

simulation software. Since the rolling stock database in FRISO already contained all rolling stock

required for the simulation area, only one parameter required adjustment: the operation profile.

This parameter could only be adjusted after inserting the desired timetable.

Insert desired timetable The third step in the setup sequence of FRISO is importing the desired timetable for the case

study defined in this research. The DONNA timetable is imported via the FRISO user interface

and only contains cyclic timetables based on a basic hour pattern (BUP). Theoretically, one BUP

is sufficient to run any simulation. The system, however, starts empty and requires a start-up time

to have a saturated network. For this reason, the timetable is expanded to five cycles.

Insert desired driver profiles After inserting the timetable in FRISO, an overview of all trains that will run through the simulation

every hour can be opened. For each train, it was specified which driver profile was used. The

possible options consist of the agent, the sportive train driver, the S-DAS based ATO, the C-DAS

based ATO and the TMS based ATO. By assigning different profiles to different trains, it was

possible to vary the percentage of train drivers that use an ATO system. To assure random

assignment, the randomizer in MATLAB was used to assign profiles to the different trains before

every experiment. These assignments were manually inserted in FRISO.

Simulation After inserting the infrastructure, rolling stock and timetable, the driver profiles can be chosen.

With this information, FRISO will run simulations and provide data files with the required data.

These data files, however, are affected by the limitations and assumption of the application

(Section 6.4).

As stated in Section 6.2, FRISO does not provide any graphical output but produces data files

instead. The first data file provided by FRISO is the OTT logging, which contains information

regarding the realized and planned train schedules: train number, replication date, realized arrival

46

time, realized departure time, arrival delay, departure delay, sub-area name, track name and train

type.

In addition to the OTT logging, FRISO provides safety logging that contains information regarding

red signals: replication number, time, situation, train series, signal name, signal route, sub-area

name, area name, operating type, name of the current route, distance, cause and description of

the situation.

More information regarding the obtained data can be found in Appendix D.

Since FRISO only provides data files and no statistically supported results, two additional steps

are required before the data can be adjusted. First off all, one needs to make sure sufficient data

samples are generated before reliable results can be generated. Second, the warm-up period

needs to be removed from the data. Section 8.1 will elaborate on these aspects. In Section 8.2,

the MATLAB script behind the data representation is briefly explained, followed by the results in

Sections 8.3 to 8.6. Section 8.7 concludes the chapter with an evaluation of the collected data.

8.1 Data collection and adjustment Before results are generated, the data need to be collected and adjusted. First off all, one needs

to make sure sufficient data samples are generated before reliable results can be generated.

Second, the warm-up period needs to be removed from the data. This section elaborates on both

aspects.

Data collection

The simulations ran by ProRail regarding driver advisory systems [46] used 50 simulations for the

same simulation area as the case study discussed in this report. For this reason, fifty experiments

were done with the sportive train driver. The data obtained from those simulations is used in to

estimate the sample size as given in [55]:

𝑛 = 𝑡𝛼 ∙ (𝜎2

𝛿2)

where 𝑛 is the minimal number of experiments, 𝑡𝛼 the t-value given confidence level 𝛼, 𝜎2 the

variance and the specified precision of the estimate. Given the desire to have a significant

confidence level, the 𝛼 is set to 0.005 and 𝛼 is set to 0.01. After inserting this formula and the

simulated data in MATLAB, the minimal number of experiments turned out to be 23.

The MATLAB code is found in Appendix C.3.

Data adjustments

In case a simulation starts with an empty model, it takes a certain amount of time before it

reaches steady state. According to [56], the most used technique to tackle this problem is called

'warming-up the model' or 'initial data deletion'. The idea behind this technique is to delete a

predefined number of data points from the beginning of the simulation run and use the remainder

for data analysis.

47

For the simulation conducted in this master thesis, the first hour is considered warm-up. After one

hour, every train that enters the simulation area encounters a full system. In addition, all trains

that did not enter in a full simulation already left the simulation.

8.2 Matlab implementation Since a lot of information is logged in both files, it is important to sort the data. For both the OTT-

data and the safety data, MATLAB creates a list of files that will be looped to acquire all

necessary data. This allows comparison of the different operating strategies. Since the data use

formats not familiar to MATLAB, they are converted to cells and stored in different arrays. Here,

special attention is paid to the conversion of strings as they are most difficult to read.

For the OTT-data, the unique trains are specified after data conversion. This parameter allows

the time-distance plot (TD-plot) to be realized for each individual train. In addition, it enables the

combination of different runs at a later stage. To create a TD plot, the script converges the

available track information in plot-able arrays. For validation reasons, the x-axis of the TD-plot is

chosen to be $HSGHTN-HSHMDO-HSHFDO-HFDM-HFD-SHL-ASRA-ASDL-AEG$. More

information regarding the validation is provided in Section 8.3.

Following the determination of the x-axis of the TD-plot, the y-axis is determined. Here, the arrival

and departure times are converted to seconds as it allows for easier processing. Subsequently,

the arrival and departure data are split per simulation run. Now, the average departure and arrival

times, as well as the 10 to 90 percentile values, can be determined. To finish the data preparation

of the TD-plot, the first hour of simulation is removed to comply with the Welch Theory. Now, all

data are available to construct the TD-plot and saved in a $.mat$ file.

To provide more insight on the situation, per operation profile, and acquiring the pre-defined

punctuality and capacity, KPI's additional information is stored. This information concerns the

average delay at Schiphol and Hoofddorp, as well as the average 10th and 90th percentile

information.

For the safety logs, the first hour of operation is removed directly after data conversion.

Subsequently, the information is separated for the different severity levels. The amount of

occurrences is counted for every situation and averaged over the amount of simulation runs. The

script concludes with the determination and plotting of the confidence interval of the results.

The Matlab scripts are found in Appendix C.

8.3 Results: Validation To verify the implementation of this case study and data processing scripts, the simulation results

will be compared the results from the report: “Trein op Lijn, de potentie van een DAS” written by

D. de Vries [46]. The report investigates the potential benefits of driver advisory systems and

models those as automatic trains with an acceptance of 100% in FRISO.

Since the study by D. de Vries [46] had a broader scope, only the results used for the goal of this

thesis are considered. This concerns the number of red signal stops, the number of red signal

approaches, the arrival delay at Schiphol and Hoofddorp, as well as the time-distance plots for S-

DAS based ATO, C-DAS based ATO and TMS based ATO. The safety plots for all three driver

profiles are provided in Figure 12.

48

Figure 12 - Amount of red signal stops and approaches per profile

Table 7 gives the amount of red signal stops and approaches for both studies, converted to the

same time format.

Table 7 - Number of red signal stops and approaches.

Amount according to simulation Amount according to [46]

SDAS CDAS TMS SDAS CDAS TMS

Red signal approach 56 49 42 55.86 48.74 42.28

Red signal stop 9 9 7 9.01 8.86 6.72

As can be seen in this table, the values are similar, which means the analysis done regarding the

safety part is done correctly.

For the next comparison, the time-distance plots for S-DAS based ATO, C-DAS based ATO and

TMS based ATO are used. The amount of data is too big for one-by-one comparison. For this

reason, the diagrams are compared visually together with the author of the previous study. It was

concluded that the plots are similar and experiments thus correctly executed. All plots of [46] are

found in Appendix E, and the plots generated in this study are found in Appendix F.

For the punctuality, the results of this master thesis are visualized in Figure 13. For comparison,

the values of both studies are summarized in Tables 8 and 9. Again, the values align well, which

implies the implementation was successful.

Figure 13 - Arrival delay Schiphol and Hoofddorp [10th to 90th percentile]

49

Table 8 - Arrival delay percentiles Schiphol.

Percentile 10th

20th

30th

40th

50th

60th

70th

80th 90th

Simulation SDAS -26.3 15.3 32.0 48.0 58.9 69.6 80.4 102.9 151.4

Simulation CDAS -6.6 16.8 33.1 48.9 61.2 70.7 81.4 103.7 151.7

Simulation TMS 23.3 36.0 47.2 55.0 63.3 80.1 95.9 117.1 156.1

TOL SDAS [46] -26.1 15.3 32.0 48.0 59.0 69.6 80.4 102.9 151.3

TOL CDAS [46] -6.8 16.6 33.1 48.9 61.2 70.8 81.4 103.7 151.7

TOL TMS [46] 22.5 36.0 47.2 55.2 63.3 80.1 95.9 117.2 156.1

Table 9 - Arrival delay percentiles Hoofddorp.

Percentile 10th

20th

30th

40th

50th

60th

70th

80th 90th

Simulation SDAS -26.3 15.3 32.0 48.0 58.9 69.6 80.4 102.9 151.4

Simulation CDAS -6.6 16.8 33.1 48.9 61.2 70.7 81.4 103.7 151.7

Simulation TMS 23.3 36.0 47.2 55.0 63.3 80.1 95.9 117.1 156.1

TOL SDAS [46] -26.1 15.3 32.0 48.0 59.0 69.6 80.4 102.9 151.3

TOL CDAS [46] -6.8 16.6 33.1 48.9 61.2 70.8 81.4 103.7 151.7

TOL TMS [46] 22.5 36.0 47.2 55.2 63.3 80.1 95.9 117.2 156.1

8.4 Results: Safety As stated in Section 6.1, non-green signal stops and approaches are regularly used as a safety

parameter and can be separated in three different variables: stopped before red signal, start

braking for red signal and red signal approach. The planned stops are not counted in any of these

variables.

Due to the limitations of the simulation software, the parameter “start braking for red signal”

cannot be logged and is thus not used in this analysis.

Figure 14 visualizes the average amount of red signal stops and red signal approaches per hour.

In addition, the 99% confidence interval is added. Table 10 provides the same information in table

format.

Table 10 – amount of red signal stops and approaches with the respective standard deviations

Driver profile

Next signal red Stopped before red signal

Mean Confidence interval [99%]

Mean Confidence interval [99%]

Sportive 62.57 1.87 12.70 0.67

25% S-DAS 61.77 1.98 11.79 0.67

50% S-DAS 60.17 1.86 10.87 0.69

75% S-DAS 58.13 1.74 9.93 0.71

S-DAS 55.82 1.53 9.04 0.66

C-DAS 48.70 1.57 8.89 0.67

TMS 42.43 1.66 6.77 0.61

IC S-DAS 56.62 1.51 9.12 0.67

Sprinter S-DAS 62.16 1.81 12.50 0.65

50

Figure 14 - Safety data: amount of red signal stops and approaches with the respective 99% confidence interval

As seen in Figure 14, the amount of trains that stopped before a red signal or approached a red

signal (next signal red) decreases with an increase in acceptance of automation. In addition,

incorporation of re-scheduling methods enhances this effect.

To determine whether or not these results are significantly different, an ANOVA analysis was

executed for both parameters and gave significant results two times. These results are visualized

in Figure 15 and Figure 16 as LSD test results from R as well as a visual representation. In the

visual overview crosses are used to indicate the significantly different groups

51

Figure 15 – ANOVA analysis and LSD for red signal approach

Figure 16 - ANOVA analysis and LSD test for red signal stops

TMS based ATO is significantly different from every other driving profile, as shown in Figure 15

and Figure 16. In addition, one needs a certain level of acceptance for automatic train operations

in order to have benefits with regards to safety: an S-DAS based ATO acceptance of 25%, for

example, will not be significantly better than the sportive driver. In addition, this study shows that

adding ATO equipment to sprinters only, shows no benefit while only equipping intercity trains

shows the same benefit as complete acceptance. Finally, it can be observed that the acceptance

has a rather linear effect on the performance improvements.

52

8.5 Results: Punctuality The punctuality is evaluated with the absolute arrival lateness in the two major train stations of

this research: Schiphol and Hoofddrop. The data visualized using the 10th till 90

th percentiles of

train arrivals are plotted next to the on-time criterion of 3 minutes. Figure 17 shows the delays in

Schiphol and Figure 18 shows a graphical representation of the delays in Hoofddorp.

Figure 16 - Arrival delay Schiphol

Figure 17 - Arrival delay Hoofddorp

Tables 11 and 12 provide the numerical values for Schiphol and Hoofddorp.

53

Table 11 - Percentile delay [10th to 90th] Schiphol per driver profile.

Percentile Agent Sportive 25% S-DAS

50% S-DAS

75% S-DAS

S-DAS C-DAS TMS IC S-DAS

SPR S-DAS

10th 17,14 -18,99 -10,52 -2,13 8,46 19,12 16,85 16,08 12,66 -13,4

20th 50,75 -1,29 7,09 18,97 24,67 31,43 26,86 26,13 25,55 9,23

30th 67,34 10,74 21,56 29,93 36,14 40,7 33,39 33,35 37,18 22,72

40th 83,7 25,94 31,77 38,21 44,67 48,75 43,13 39,17 45,24 33,6

50th 95,56 41,62 45,28 47,58 51,96 56,1 53,58 44,59 51,68 46,22

60th 110,22 56,76 58,81 62,9 65,15 69,23 68,49 50,55 63,2 64,03

70th 129,68 71,2 72,44 72,87 74,51 75,3 77,8 62,78 75,1 73,21

80th 148,59 80,83 81,48 82,21 82,96 83,32 84,18 74,75 82,77 81,34

90th 173,86 95,99 96,4 95,39 95,51 95,59 95,24 92,24 94,94 96,38

Table 12 - Percentile delay [10th to 90th] Hoofddorp per driver profile.

Percentile Agent Sportive 25% S-DAS

50% S-DAS

75% S-DAS

S-DAS C-DAS TMS IC S-DAS

SPR S-DAS

10th -2,26 -33,26 -32,75 -32,75 -26,36 -6,6 23,32 15,04 -6,62 -32,75

20th 56,33 8,51 11,21 11,37 15,34 16,77 35,97 28,53 16,44 8,59

30th 80,98 28,24 28,96 31,64 31,99 33,15 47,24 37,82 32,16 30,24

40th 95,74 45,67 45,47 48,07 47,99 48,9 54,94 44,45 45,74 46,24

50th 115,32 53,26 56 57,9 58,94 61,2 63,28 51,76 57,62 56,98

60th 132,43 64,27 66,52 67,8 69,59 70,69 80,07 73,97 70,48 64,97

70th 149,64 76,69 78,04 78,54 80,36 81,36 95,93 94,15 80,21 77,16

80th 186,62 101,32 102,01 102,22 102,86 103,7 117,14 116,51 102,44 102,08

90th 243,38 148,49 149,11 150,41 151,44 151,66 156,1 145,38 151,66 148,82

As seen in Figure 17 and 18 as well as Tables 8 and 9, the first percentiles are rather scattered,

while they converge later one for all driving profiles but the agent. This effect occurs when the

automatic train controllers aim to arrive with an absolute delay of zero seconds and when the

train arrival is expected early. Since early arrivals can be taken out easily by reducing the speed,

no extremely early arrivals are recorded. For the sportive train driver on the other hand, early

arrival is expected in case of empty tracks and no disturbances, as it does not use the 10%

margin in the timetable but just drives as fast as possible. This tendency makes the sportive

driver have the least delay of all driver profiles up till the 50th percentile in Schiphol and

Hoofddorp. In practice, this might also not be optimal since it leads to very long dwell times and

more unplanned red signal approaches and stops.

The converging part of the graph most likely is due to the high amount of rail traffic in the

Schiphol Area. With the larger delays, trains get so close they have to reduce speed to prevent

red signal approaches and stops. Here every profile performs approximately the same, since the

train is stuck in some kind of traffic jam and there is no rescheduling mechanism that can help

here.

The major reason why the agent does not follow any trend is because it does not do anything

specific but is a combination of all kinds of real train drivers that drive in the Netherlands.

54

8.6 Results: Capacity To evaluate the change in capacity, the deviation of the arrival time along all checking points of

the track is used. Time-distance (TD) plots are used to visualize these data. Since this method

requires different plots for every driver profile, only the extremes are shown in this section. The

TD-plot for every separate driver profile is found in Appendix F.

Figure 18 shows the TD-plot of the used baseline: the sportive driver, and Figure 19 shows the

TD-plot of the TMS based ATO driver.

Figure 18 - TD plot sportive driver

Figure 19 - TD plot TMS based ATO

55

To have a clearer overview, comparative plots that contain the average standard deviation per

driver profile are generated as shown in Figure 21. In addition, the train dispersion per operation

type is given as percentage provided in Figure 22. Here, the sportive train driver is taken as

baseline. The information of Figures 21 and 22 is shown numerically in Table 10.

Figure 20 - Average standard deviation per operation type.

Figure 21 - Average standard deviation per operation type as percentage of the sportive driver.

56

Table 13 - Deviation values per driver profile in both minutes and percentages.

Sportive 25% S-DAS

50% S-DAS

75% S-DAS

S-DAS C-DAS TMS SPR S-DAS

IC S-DAS

Total STD [min] 0,71 0,70 0,68 0,66 0,65 0,64 0,58 0,66 0,70 IC STD [min] 0,75 0,74 0,72 0,70 0,68 0,65 0,60 0,68 0,75 Sprinter STD [min] 0,64 0,63 0,63 0,62 0,61 0,62 0,55 0,64 0,61 Total STD [%] 1,00 0,99 0,96 0,93 0,91 0,9,0 0,82 0,93 0,99

IC STD [%] 1,00 0,99 0,96 0,92 0,90 0,860 0,80 0,90 1,00

Sprinter STD [%] 1,00 0,99 0,98 0,96 0,96 0,98 0,86 1,00 0,96

Figure 22 - ANOVA analysis and LSD test for train capacity.

The TD plots show significant differences in train arrival time deviation, as visible in Figures 18 till

21. As with safety, the TMS based ATO driving profile stands out in performance, showing that

the incorporation of well-defined re-scheduling rules has significant benefits. In addition, the

acceptance shows similar behavior when it comes to capacity as it does with the safety: the

acceptance seems to have a linear correlation with the capacity improvements. To determine

whether or not these results are significantly different, an ANOVA analysis was executed and

gave a significant result. To visualize this, an LSD test is executed and shown in Figure 22.

Again, it is shown that the level of acceptance needs to exceed a certain threshold before any

capacity benefits can be observed.

It is interesting to see that the S-DAS based ATO and C-DAS based ATO do not significantly

differ in this study case, based on the capacity criterion. Finally, equipping sprinters with ATO

does not benefit the capacity nearly as much as equipping intercity trains. One possible reason

for this observation might be the overall lower arrival deviation in sprinter trains compared to

intercity trains, meaning less compensation is required.

57

8.7 Case study evaluation Overall, the study shows that both the incorporation of re-scheduling methods and the

acceptance of ATO improve the train performance, given the set performance indicators on the

Dutch railway network. Given the results of this study, the major benefits are in safety and

capacity, while punctuality does not improve a lot. In addition, one can say that a certain level of

acceptance is required before significant benefits can be witnessed. Furthermore, this study

implies that the use of ATO for intercity trains alone provides the same benefits as equipping both

intercity trains and sprinters.

The results found in this study align with the expectations given by academic literature as the

capacity and safety showed improvements with the use of ATO. In addition, the introduction of re-

scheduling enhanced the positive effect of automatic train operations significantly.

The importance of user acceptance has to be addressed as well. Academic literature clearly

shows that committed and motivated employees are necessary for the success of a company.

[17] states that a motivated employee is more willing to try new technologies and help to improve

them. The simulations ran for this master thesis show similar results: without the acceptance of

automatic train operations, the benefits in safety and capacity will drop significantly.

Though benefits in safety and capacity are found, the punctuality did not improve with the use of

automatic train operations. As stated in [17], many studies showed that the use of ATO and / or

re-scheduling enhances the performance in disturbed situations, while this study did not. One of

the causes of this unexpected result is the utilization of the area around Schiphol airport.

Especially with increased delay, the trains might be packed so tight together that the automatic

train protection only allows the train to move one block at a time. This restriction diminishes all

positive benefits of ATO and re-scheduling since the only possible action is slowly going to the

next block. As for the lower percentiles, the system is optimized to be exactly on time, which

means not too late but also not too early. For that reason the train will initially drive slower and not

have time to react in time to incoming delays, resulting in an overall higher delay.

58

9. Conclusions & discussion

The final chapter elaborates on the main findings of the field research in Section 9.1 and the

study case in Section 9.2. Section 9.3 continues with answers on sub-questions, based on these

findings. The chapter concludes with the discussion and answer to the research question in

Section 9.4 and the recommendations in Section 9.5.

9.1 Main findings from the field research From the field research it can be concluded that following implementation methods proposed in

academic literature lead to a smooth introduction of automatic train operation. Not following the

introduction methodology prescribed in academic literature, however, turns out to not be as

ineffective or problematic as some papers or authorities claim them to be.

The introduction of automatic train operations in Czech Republic did not face significant

resistance, despite the big changes it made to train driver tasks. To achieve this, the national

railway infrastructure manager and railway operators, built ATO compatible trial tracks and

actively gathered train driver feedback to optimize the user experience. In addition, ATO has not

been obliged to any of their employees, nor were there any financial repercussions for not using

the ATO system. Heretofore, the network keeps being expanded, while keeping the same

philosophy regarding the use of ATO towards both new hires and senior employees.

In the United Kingdom another approach was employed. First, the department of transport

merged all rail operators that use the Thameslink into one franchise. Second, a fleet of new ATO

equipped trains was bought to operate on the Thameslink area. Every user was obliged to use

these new trains and had a couple of years to familiarize their staff with manually operating the

new rolling stock. Every company that uses the new trains receives a bonus, while every

company that refuses is not allowed to continue service after December 2018. After

familiarization with manual operation of the new trains, operators are trained to use the automatic

train operation and obliged to use this type of operation on the designated areas after the project

finished in 2018. To compensate for this sudden change, train drivers receive a financial

compensation and are no longer responsible for the trains’ safety in the area where ATO is

active. Though the Thameslink Programme is nearly completed, no strikes with regard to

automation have occurred.

9.2 Main findings from study case From the study case can be concluded that the incorporation of more advanced re-scheduling

techniques and the acceptance of ATO have a significant positive influence on the arrival time

reliability (capacity) and the safety, but that the effect on punctuality is limited.

When comparing the profiles on a safety level, it shows that a minimal acceptance of 50% is

required to achieve a significantly lower amount of red signal stops, and 75% to have a

significantly lower amount of red signal approaches when compared to the sportive driver as base

line. The study also shows that implementing S-DAS based ATO for sprinter trains only has no

significant effect on either the red signal stops or approaches, while equipping IC trains only

provides significantly the same benefit as having all trains equipped with S-DAS based ATO.

59

It is also shown that the incorporation of more advanced re-scheduling significantly reduces the

amount of red signal stops and approaches. TMS based ATO equipped trains with 100%

acceptance perform significantly better than any of the other driver profiles evaluated in this

study. C-DAS based ATO provides safer operation than less advanced profiles without any re-

scheduling, but does not result in significantly less red signal stops.

When comparing arrival time spread, similar observations are made as with safety: 75%

acceptance is required to get a significantly smaller spread in arrival time when compared to the

sportive driver. Equipping sprinter trains with S-DAS based ATO does not significantly reduce the

arrival spread, while equipping IC trains provides similar results to the 100% accepted S-DAS

based ATO profile. TMS based ATO, again, out-performs any other profile, while fully accepted

C-DAS based ATO performs significantly the same as S-DAS based ATO with 75% and 100%

acceptance.

The absolute arrival lateness at Schiphol is similar across all automatic train operation profiles

from the 50th percentile and higher, after which all these profiles converge to one point. At

Hoofddorp, the absolute lateness is similar across all profiles from the 40th percentile onwards,

after which most profiles converge as well. At lower percentiles, such as the tenth percentile,

profiles with the least advanced re-scheduling mechanism and a low acceptance percentage

arrive first and show the least delay. Overall, the sportive train driver arrives first in up till the 40th

percentile, followed by the profiles that have biggest share of the sportive train driver. For the

punctuality criterion, the agent is an outlier for both Schiphol and Hoofddorp and does not follow

any of the trends described in this paragraph.

9.3 Feedback on the research questions Within the sub-questions, a distinction was made between technological acceptance and simulation. The first two sub-questions are answered with the use of Chapters 3 till 5, while Chapters 5 till 8 support the questions regarding simulation. This section provides an answer to all sub-questions defined in Chapter 2. “How do the findings regarding technological acceptance of ATO relate to the information available in academic literature?”

From the academic literature summarized in Section 3.1 and Section 3.4, one knows which

factors need to be taken into consideration during the integration of partially automated systems.

First of all, employees need to be engaged in their jobs. Here, perceived control and a sufficient

amount of job resources are key. This can be achieved by a dedicated selection during the hiring

process, incorporating train drivers in the early stages of automation, relying on managers with

strong leadership skills, as well as clear communication with the future user. Second, one needs

to consider how automation makes the users perceive themselves. Section 4.4 states that Dutch

train drivers are proud of their jobs, while Section 4.2 states that the majority of the train drivers

see themselves as perfect at their jobs. It is of most importance to not tarnish these feelings,

since it will most likely lead to strikes, especially with the Dutch train driver base it is very

demanding. The academic literature summarized in Section 3.3 supports this observation. Based

on the theory, one can say that this pride, variety and engagement are the most important factors

to consider.

Following the content of Section 5.1 and the literature study, one can say the Czech Railways

practiced an operator-friendly way of implementation. The use of ATO is not mandatory, nor does

it give any negative consequences in case a driver does not use it. In addition, train driver

feedback was actively gathered to improve the driver machine interface as well as the user

60

experience. Overall, the introduction of ATO in the Czech Republic was a big success. Despite

the outcome, it has one major flaw: It took several decades to implement this system. Heretofore,

the implementation is still not finished.

As can be read in Section 5.2, the implementation of automatic train operations on the

Thameslink is based on a completely different methodology: After merging all rail operators on

the Thameslink into one franchise, they were obliged to operate a prescribed train-type and use

ATO on the core area after the Thameslink Programme finishes. Every participant that refuses

these demands is not allowed to operate any services after December 2018. As compensation

train drivers and participating railway operators are financially compensated. This approach goes

against the literature described in Section 3.4, but only requires several years to complete and

hasn’t seen any strakes up till today.

From all evaluated cases, Deutsche Bahn has the biggest ambition: completely eliminating the

train driver, which basically translates to skipping at least ATO GoA2 as used in Czech Republic

the United Kingdom. This jump in automation grades embraces the real achievement of

automation even more than the introduction of GoA2. Train personnel will only focus on what

humans do best; servicing the passengers and anticipating on unexpected situations. This

change, however, goes does not align with the prescribed literature at all. For now, no deadlines

have been planned, nor have actions been taken, but I expect that a lot of railway employees will

boycott these changes if they come to fruition as their jobs are directly on the line.

The only Dutch initiative evaluated during the field research is the RolTijdapp, discussed in

Section 5.4. The Netherlands Railways successfully piloted a driver advisory system without any

resistance as first step in direction of train automation. Like the case described in Section 5.1, the

implementation strategy incorporates train driver feedback, trial periods and two year of

familiarization. Given that this app only advices train drivers on one aspect of their tasks, the

introduction of automatic train operations will most likely at least take one more decade.

The introduction of the RolTijdApp might be in line with the literature as people keep their

freedom and use their craftsmanship the way they like, but the time it takes to gradually introduce

ATO might be too much for the railway branch.

In Section 5.5, a completely different approach of railway operation is proposed to compete with

other modalities. Instead of the common idea of a long-haul rail transport following a predefined

schedule, smaller pods are presented as one of the last available options for the railway industry

to compete with road transport. These autonomous, agile and responsive railway pods without a

set schedule are completely autonomous and can be introduced at remote areas with specific

demand for such solutions. Port regions such as Antwerp, Salzburg or Hamburg are prime

examples as they all expect more freight in the coming years. From these remote areas, the

network can be expanded gradually. This method is not covered by academic literature as little

research has been done on this topic and its introduction does not challenge any jobs.

“Given these findings, how to improve the technological acceptance of automatic train operations?”

Throughout this master thesis, four different strategies have been evaluated. The first strategy aims at a smooth and phased introduction with several feedback loops and long transition period. This strategy is supported by academic literature and significantly improves the technical acceptance but takes a long time to reach the final state in which all trains operate automatically. In addition, a lot of financial resources are required to facilitate the process.

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The second strategy is more direct and obliges the user to use automatic train operations (GoA2)

on the designated area after the surrounding facilities such as the traffic management system and

infrastructure have been finished. To compensate for inconveniences and sudden changes, the

train drivers and railway operators are compensated in a financial way. This strategy is not

supported by academic literature, integrates automatic train operation within several years and

has not seen any significant resistance up till now. After the current program is finished, it is

possible to gradually expand the area in which ATO has to be used and later upgrade the grade

of automation.

The third strategy is similar to the second one as it aims at substituting the train driver without any

smooth transition period. The third strategy, however, takes it one step further and aims at

completely eliminating the train driver from the system. This strategy, again, does not follow the

gradual implementation as explained in academic literature and has a bigger challenge than

slightly changing the train driver tasks and removing certain challenges. As stated in Section 3.2,

ATO GoA3/4 requires a completely different kind of employee. Though a fully automated network

is most likely realized faster than with the first approach, the strategy is not complete.

The fourth and final strategy aims at ATO introductions in small and more remote places that

require innovative transportation to move around passengers or freight. Like the second strategy,

the network starts small and can expand over time. This strategy is not supported by academic

literature, but does not involve any task changes as it is introduced on a new location. Because of

its need, the initial implementation time is expected to be several years, just like the second

strategy.

Given academic literature, the first strategy seems the safest and most logical choice. This

method, however, has one major flaw: the implementation time. The raising threat of sustainable

and environmentally friendlier road options is real and generally not considered in any of the

papers as those are often not specifically scooped on the railway industry.

In its basis, every company aims to earn the most money possible given the available resources.

Based on the current demands and the challenges in the railway industry, I think a faster phased

introduction will improve the changes to overcome the challenges. On top of that, a faster phased

transition most likely costs less money in the long run as not as many man-hours are required to

facilitate all support, trials and feedback iterations.

Given the profiles described in this master thesis, I recommend a combination of method two,

three and four. The specific demands of remote areas, such as large ports, allow for a flexible

and safe testing area with relatively low cost. In addition, the standing demands speeds up the

development of the appropriate and reliable train controller and the traffic management system.

After successfully realizing a fully automated area, this knowledge can be used to upgrade one or

multiple bottlenecks in the main heavy rail network.

As implementation strategy, I recommend to use an approach similar to the second strategy but

with autonomously operating trains in the bottleneck area. Given the mentality of the Dutch train

driver, this might imply a certain form of resistance. I, however, think that the small area of

operation as well as a financial compensation can reduce the severity of this challenge. This

approach showed great success during the Thameslink Programme in the United Kingdom.

Since several long-haul trains will cross the bottleneck area, one is able to gradually upgrade all

rolling stock, which subsequently allows for an easier introduction in other bottleneck areas since

not all rolling stock needs to be upgraded. On top of that, a reasonable amount of train drivers will

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already be trained and familiar with the new way of working. I expect this to lead to less

resistance and even more opportunities for expansion.

Based on this section, the following approaches have the ability to improve the technical

acceptance of automatic train operations:

Use an area with a direct demand for improved transport of either freight or passengers. A positive business case opens up more resources and allow for faster development of ATO profiles and the traffic management system.

Use a new site or an area that does not employ any train drivers to completely remove any resistance.

Gradually expand the network instead of gradually increasing the grade of automation. This way the change doesn’t seem as big and scary on a national scale. In addition, the pool of trained personnel can gradually increase with the network.

Restrictions or a reduction in train driver joys can be compensated in other areas (salary, job resources, operator deployment model).

The past experiences improve the change of success of the next expansion. This confidence results in less doubt and resistance and a better business case.

The bullet points do not include the commonly known factors such as perceived control, operator support, adapted recruitment, variety and pride. Though any of these factors will contribute to a smoother transfer to higher grades of automation, they all require a lot of resources and time. Given the high demands and rapidly improving technologies, I do not consider them to be the most suitable foundation to implement automatic train operations. “How can a simulation case study investigate if the introduction of ATO has benefits for the heavy rail

sector?”

The introduction of automatic train operation will affect the complete railway network of the

Netherlands, since every train will benefit from improved punctuality, safety and capacity. Since

investigating the complete railway network of the Netherlands is infeasible, a case study is

executed to investigate the effects of ATO on the performance indicators in the Netherlands. To

be able to extrapolate the findings of the case study to a broader area, the case study has to be

chosen carefully.

To obtain reliable results without testing unknown technologies on real trains, a microscopic

simulation has to be executed. The major railway bottleneck in the Netherlands has been used to

determine whether or not the introduction of ATO results in benefits in the form of capacity, safety

or punctuality improvements. To evaluate the safety performance of ATO, the number of red

signal approaches and stops has been used. These numbers are frequently used for this

purpose.

In order to evaluate the capacity possibilities, the consistency of the arrival time is used. As stated

in the Thameslink Programme, trains with a high arrival time consistency and thus a low spread

(standard deviation) in arrival time are more predictable than trains with a high arrival time

spread. This predictability can then be used to reduce the minimal train distance and thus

increase the capacity.

Finally, punctuality is evaluated by means of average arrival delay at the two major train stations

in the simulation area. Together, these performance indicators should be able to provide support

regarding the performance of ATO in comparison to other methods.

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“Which simulation tool can be used for this simulation?”

To simulate the case study in with the required details, a microscopic simulation too is necessary. ProRail and Netherlands Railways currently make use of three different tools: RailSys, FRISO and OpenTrack. All three applications can execute the simulations and will generate similar results. Based on workability and availability, RailSys is the preferred tool for this master thesis. Because FRISO is designed and developed by ProRail, most knowledge is available within the organization. In addition, it already includes a lot of data and contains all necessary modules. This will save both time and effort when setting up the simulation software.

“What is the effect of (partial) acceptance on the operational benefits of automatic train operations?”

The simulation showed that an increased acceptance of S-DAS based ATO results in a reduced

amount of unplanned red signal approaches and stops. To significantly lower the amount of

unplanned red signal stops, an acceptance of at least 50% is required, while an acceptance of at

least 75% is required to have a significantly lower amount of red signal approaches. Both in

comparison to the sportive train driver. The simulation also shows that an acceptance of 100%

does not significantly lowers the amount of unplanned red signal stops or approaches. In case

only sprinters are equipped with ATO, the results do not significantly differ from the base profile.

For intercities, on the other hand, the results are in the same category as 75% and 100% of S-

DAS acceptance. These results imply that the train safety improves significantly increases when

at least 50% of the train drivers or all intercity train drivers accept the automatic train controller.

For arrival time deviation, the results and improvements were similar to the amount of unplanned

red signal approaches: 75% acceptance is required to get a significantly smaller arrival deviation

than the sportive train driver. In addition, equipping sprinter with S-DAS based ATO does not

significantly reduce the arrival spread, while equipping IC trains provides similar results to the

100% accepted S-DAS based ATO profile. These results imply that the train capacity over the

Schiphol Area will be significantly increased when at least 50% of the train drivers or all intercity

train drivers accept the automatic train controller.

The fact that the introduction of ATO improves the safety and capacity isn’t a surprise as a similar

effect is observed in the mass transit rail. The required percentages, on the other hand, are

higher than expected. Many colleagues expected ATO to have a significant effect at a percentage

of 25%. Their theory is similar to stories I heard from the automotive industry: having someone in

front of you driving in accordance with the optimal schedule enables you to drive more optimal as

well.

I expect the gap to be a result of software limitation as the previously described behavior is not

included in the simulation algorithm: The sportive driver simply tries to driver as fast as possible.

This, however, does not mean that the simulation results are incorrect as this behavior is just an

assumption and not validated.

The final performance indicator determined in this study is the arrival lateness at Schiphol and

Hoofddorp. From the 50th percentile and higher, the agent and all (partly) S-DAS based ATO

profiles converged to one point, resulting in similar arrival delays. From a passenger perspective,

increasing the acceptance percentage of ATO did not improve the punctuality. Up till the 40th

percentile, the sportive agent was the first to arrive resulting in the least passenger delay. For this

region, an increased amount of acceptance results in an increased delay.

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That the punctuality does not improve with an increase acceptance does not align with academic

literature written about ATO in the mass transit rail sector, but is still not unexpected. The S-DAS

based ATO profile is designed to minimize the absolute delay based on the data at departure,

which means the controller aims at an arrival delay of zero seconds and does not change the

profile over time. Because of the disturbances added in this simulation this leads to non-

compensable arrival delays, whereas the sportive driver just aims to drive as fast as possible and

can make up for certain portions of the delay. For higher percentiles, all profiles give the same

result due to the high amount of trains in the simulation. The trains are stuck between other trains

and not able to operate at the desired speed, making any predefined operation speed unusable.

All in all, the effect of (partial) acceptance of automatic train operation is of high importance for

the safety and arrival time deviation of the train. An acceptance of at least 50% and 75%,

respectively, is required to observe a significant difference in these performance indicators. For

the arrival times in Schiphol and Hoofddorp, no improvements are found in this study because of

the controller design chosen for this study.

“Is there any operational benefit of incorporating the expected operational train data, such as arrival time,

in the planning strategy of automatic train operation?”

The simulation shows that fully accepted TMS based ATO systems has significantly less unplanned red signal approaches and stops than any of the other driver profiles. In addition, it has a significantly lower arrival time deviation as well, making it the best performing profile used in this master thesis on these two performance indicators. For punctuality, on the other hand, the performance does not exceed any of the less advanced profiles. For lower percentiles, TMS based ATO has a larger delay than most other profiles. For the percentiles of 50 and higher, on the other hand, the arrival lateness is similar to that of all the other profiles. Given academic literature on re-scheduling algorithms for small and closed railway networks, the TMS based ATO profile was expected to outperform the other profile on safety, capacity and punctuality. For safety and capacity, these expectations were fulfilled. For punctuality, on the other hand, no improvement was observed. Like with S-DAS based ATO, this is a result of the profile design. TMS based ATO is designed to minimize the absolute delay given its and other trains locations and expected arrival times. This means that, every two seconds, the system determines its optimal speed to arrive with a delay of zero seconds. As a result, the train drives slower in case it expects to have a lot of time left. When encountering a delay, compensations are made and a new speed is calculated. Due to the maximal allowed speed set by the protection system, this schedule might not be feasible and delays cannot be (completely) compensated. This results in for delays in the lower percentiles. For higher percentiles, the situation for TMS based ATO is exactly the same as for the other profiles: due to the high amount of trains on the network, trains cannot operate freely and are thus not able make use of the suggested speed adjustments. For C-DAS based ATO, results similar to TMS based ATO were expected. Simulation, however, showed that this was not the case. Though C-DAS based ATO shows significantly less red signal approaches than any S-DAS based profile, it shows similar results to 100% and IC accepted S-DAS based ATO for the amount of unplanned red signal stops and the arrival time deviation. This means that the incorporation of additional data is only beneficial if the system has enough space to use it [being able to change sequence, less trains, more tracks.]. Since TMS and C-DAS based ATO share the same objective function, C-DAS based ATO has a similar punctuality performance as the other automated profiles.

65

Overall, TMS based ATO out-performs all other driving profiles on the capacity and safety criteria. C-DAS based ATO, however, turned out to be a slightly safer version of S-DAS ATO with a lot of additional cost and required data traffic.

9.4 Discussion and answer to the main research question This research project provides insights in the different approaches to introduce automatic train

operations in the railway industry as well as the effects of partial acceptance on the performance

of automatic train operations. Given these insights and effects, the research question formulated

in Chapter one can now be answers:

“Under what conditions and to what extent can train performance be optimized with the use of

intelligent automation in disturbed networks?”

The simulation showed that an increased acceptance of S-DAS based ATO results in a reduced

amount of unplanned red signal approaches and stops. To significantly lower the amount of

unplanned red signal stops, an acceptance of at least 50% is required, while an acceptance of at

least 75% is required to have a significantly lower amount of red signal approaches. For arrival

time deviation, the results and improvements were similar to the amount of unplanned red signal

approaches: 75% acceptance is required to get a significantly smaller arrival deviation than the

sportive train driver. These results imply that the acceptance percentage has to be at least 50%

to have significantly improved performance, making train driver acceptance crucial for the

performance of ATO.

From the theory, it was concluded that the technological acceptance is strongly connected to

engagement (perceived control, operator support and adapted recruitment), variety and pride. All

decision made during integration of ATO should incorporate either of those elements to improve

the technological acceptance of higher levels of automatic train operations. The most prominent

adjustment, however, is the incorporation of the train driver in every stage of the designing,

implementation and evaluation phases. This way, they get a perceived feeling of power which

enhances their pride.

Though this is the theoretically most supported approach, it misses two important components:

Cost and completion time. Generally speaking, going through a lot of optimization iterations costs

a lot of human resources and thus money. In addition, evaluation and gradual implementation

costs a lot of time, as seen in the Czech Republic. Given the continuously increasing capacity,

punctuality, environmental and reliability demands as well as the fast developments in road traffic,

I highly recommend introducing forms of automatic train operation at a faster phase than done in

Czech Republic.

Before introducing ATO on the Dutch heavy rail network, I recommend to look for specific

transportation demands in remote areas, such as large ports. These areas allow for a flexible and

safe testing area with a good business case, which speeds up the development of an appropriate

and reliable train controller as well as a traffic management system with the necessary (re-)

scheduling tools and driving profiles. In addition, they do not employ any train drivers removing

the resistance altogether. After successfully realizing a fully automated area, this knowledge can

be used to upgrade a bottleneck area and gradually expand towards the main heavy rail network.

This allows for a relatively fast transition, without overwhelming the network. The amount of

adjusted or new rolling stock as well as the infrastructure adjustments is significantly smaller than

with a gradual introduction of ATO over the whole country. In addition, one only needs to train a

small group of operators to supervise the trains over the bottleneck.

66

As implementation strategy for the bottleneck area, I recommend to use an approach similar to

the one used in the Thameslink Programme, but with autonomously operating trains in the

bottleneck area (GoA3 instead of GoA2). This strategy showed fast results, without significantly

upsetting the train drivers. Given the nature of Dutch train drivers and their union, this method

may imply a certain form of resistance. The affected area, however, is rather small in comparison

to the complete Dutch railway network. In addition, financial compensation can soften the

resistance as seen in London.

Given available rolling stock and trained train drivers, the next upgrade will require fewer

resources. In the end, following this method might take the same amount of time to completely

automate the Dutch rail network as gradual implementation, but requires fewer human resources

while directly tackling the bottlenecks.

With a larger network, more advanced traffic management systems with re-scheduling

capabilities are required. As stated in Section 5.5, this is the biggest challenge in railway network

automation and thus requires the most resources. Given the performance on safety, punctuality

and capacity of the different fully accepted ATO based profiles, it is known that data incorporation

has little advantage over predefined profiles in case the train is not able to follow this profile by

means of, for example, changing the sequence. In my opinion, this means that only two ATO

systems should be considered: A very simple one like S-DAS based ATO and an advanced one

like TMS based ATO.

For cost reasons, I recommend to start with simple driver profiles and a basic traffic management

system without any data-exchange during operation outside of a train station. This way, the costs

are kept relatively low, while still enjoying the benefits of automatic train operation. After the

network matured, one can consider to significantly upgrading the network with continuous data-

exchange, re-scheduling rules and even train pods. This, again, can be tested on a smaller area

such as a bottleneck before rolling it out over a complete network. For this step, the remote area

is not required since the grade of automation will not change.

9.5 Suggestions for future research Based on the findings in this report and the answer on the research questions, several

recommendations can be made for practice and future research:

1. Real life experiment with S-DAS based ATO in a remote area.

To check whether the effect of acceptation is really approximated by a linear function, one

can gradually introduce S-DAS based ATO in a remote or closed area. In addition, one

can determine whether or not the gains are similar to the simulation results. Based on

these results, the area can be expanded or the technology improved. Aside from

validation, this study case can serve as stepping stone for larger or more advanced

implementation of ATO in the Dutch railway network.

2. Extend research

Extend the research by introducing combined profiles of C-DAS or TMS based ATO with

the sportive train driver. Both case studies provide information regarding the breaking

point of significance and can be used to adjust the policy regarding train driver

incorporation. In addition, one can try to improve the re-scheduling mechanism behind the

TMS based ATO controller to determine how much additional gain can be achieved. With

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these experiments, one can determine if the additional cost required for C-DAS or TMS

based ATO are worth their money in comparison to S-DAS based ATO.

3. Investigate all bottlenecks in the Netherlands

Within this master thesis, only one bottleneck is investigated. Since there are a lot more

bottlenecks within the Netherlands, it is advisable to research more of them to validate the

results. This gives additional insight to which extend ATO can help improving the safety

and solve the capacity problem. In addition, one can determine whether the punctuality is

really not influenced. Overall, the results of this research can support the results found in

this study and strengthen an ATO implementation business case.

4. Apply the research in other countries

The structure of this research can be used for other railway networks which also want to

investigate the potential benefits of ATO. This not only shows the reliance on a certain

infrastructure but also contributes to the overall knowledge regarding ATO. In addition to

this knowledge, the study can help the European Union and the Shift2Rail program to get

one small step closer to standardizing the European railway network.

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2012. [Online]. Available: https://www.itl.nist.gov/div898/handbook/index.htm. [Accessed

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[60] M. Zeilstra, "Intergo: Human Factors - de theorie," 2016.

72

Appendix A – Interview This appendix contains the interview held with Vladimir Kampik, from AZD Praha, and Tomas

Konopac from the SZDC on the February the 14th, 2017.

What was the main reason to introduce automatic train operations?

Reduce the workload of the train driver. Especially during winter weather conditions such

as fog, rain and snow, it is very difficult to reliably operate a train. The implementation of

ATO enables the train driver to focus on his main task: the safety of the train.

How did you approach the implementation of ATO?

We tried to take is slowly. Since the business case for ATO wasn’t as strong as for other

projects, the available budget was limited. The complete story on how we implemented is

written in a powerpoint presentation. We can provide it after this interview.

The powerpoint presentation is found in reference [37].

What do you think of ATO being very low on the priority list?

This of course is a pity. Especially after we showed it does benefit our employees as well

as the punctuality. On the other hand, we had a lot of time developing and integrating the

ATO system without anyone bothering us, which I think was a good thing.

Do train drivers need a special training to drive with an ATO equipped train?

Definitely. We have a special training program to teach people how to use these systems.

After completing it, they get a certificate that allows them to operate ATO equipped trains.

Did you witness a lot of resistance with the introduction of ATO?

Like with every technological improvement, especially the older people were a bit skeptic.

We, however, didn’t face a lot of resistance since we didn’t oblige anyone to use the new

system. We think it is important to consider possible personal problems and moods and

want people to familiarize themselves with the technology in their own way and tempo. In

addition, we mentioned how the new system will benefit the train driver. This made most

train drivers curious to try it.

What do the train drivers think of ATO these days?

They like it a lot, since it reduces their workload. Most train drivers use it over manual

driving. For safety reasons (in case ATO fails), it is important to keep practicing manual

operation every now and again. The train drivers, however, got so used to the ATO

system that they complain in case they need to drive manually as it costs a lot more effort.

73

Did you adjust the train driver recruitment process, knowing the system will be more and

more automated over time?

No. These days the amount of people that get excited over trains increases. The older

generation of train drivers we worked with generally had great passion for their jobs and

trains in general. These days, the majority of the people just come to us for the money.

We offer a reliable ad stable job, but when someone else offers them more money, they

leave. To them it doesn’t really matter what they need to do in the train as long as they

get their salaries.

74

Appendix B – Thameslink London

Thameslink

De visie van Network Rail op automatic train operations

Thameslink excursie NS ProRail.

14-03-2017

Auteur: Nico de Mooij

Deelnemers: J.H. Hoogenraaad (Jan) Consultant NS J.M. Knijff (Joke) ProRail N.A.K. de Mooij (Nico) ProRail A.A.M. Schaafsma (Alfons) ProRail P. Schouwerwou (Piet) NS H.H. Tijsma (Herman) ProRail E.L.I. Vaes (Illya) ProRail M.G.P. Bartholomeus (Maarten) ProRail

75

Lijst met afkortingen en begrippen De belangrijkste begrippen uit dit rapport worden in dit hoofdstuk afgebakend. De definitie van de

begrippen wordt in de rest van het rapport gehanteerd.

ATO: Automatic Train Operation Het ATO systeem is een subsysteem dat een aantal tot alle taken kan overnemen van de machinist. Van deze functies is het effectief en efficient rijden van de trein de belangrijkste. GoA: Grade of Automation De mate waarin een systeem geautomatiseerd is. De tabel hieronder geeft een overzicht van de levels in de spoorwereld. Note: GoA2+ betekent GoA2 of hoger.

Type of train operation Set train in motion

Stop train Door closure / opening Operation in event of disruption

GoA 0 Machinist Machinist Machinist Machinist Machinist

GoA 1 Automatische treinbeveiliging met Machinist

Machinist Machinist Machinist Machinist

GoA 2 Automatische treinbeveiliging, ATO en Machinist

Automatisch Automatisch Machinist Machinist

GoA 3 Driverless Automatisch Automatisch Train attendant Train attendant

GoA 4 Unattended train operation Automatisch Automatisch Automatisch Automatisch

ETCS: European Train Control System ETCS is een trein sein- en beveiligingssysteem, ontworpen om de verschillende nationale beveiligingssystemen in Europa te vervangen. TPWS: Train Protection & Warning System Het meest gebruikte beveiligingssysteem in het Verenigd Koninkrijk. Het systeem stopt de trein indien een geel sein is gepasseerd zonder toestemming, een geel sein met een te hoge snelheid benaderd wordt, of opkomende snelheidsbeperkingen/buffer stops te snel benaderd worden. AWS: Automatic Warning System AWS is onderdeel van het Britse seinensysteem en waarschuwt de machinist of het aankomende sein groen is of niet. De kern Het 7 kilometer lange deel van het Thameslink-traject waar 24 treinen per uur gaan rijden: St. Pancras - Blackfriars STM (Specific Transmission Module) Het systeemonderdeel dat operatie over de nationale treinbeveiligingssystemen zoals ATB en TPWS mogelijk maakt. NTC Trein uitgerust met ERTMS/ETCS rijdend op een lijn met een nationaal beveiligingssysteem TMS: Traffic Management System Het systeem dat tracht treinbewegingen te optimaliseren door intelligent gebruik van dienstregelingen en de data uit rijdende treinen. ARS: Automatische Route Setting Een systeem dat de instellingen van een gegeven route doorgeeft wanneer een trein een sein benaderd, mits het baanvakvrij is.

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Introductie Het Thameslink programma is een door de overheid gefinancierd initiatief ten behoeve van de

capaciteitsverhoging op de forensroutes in en door het centrum van London. De doelstellingen

worden gerealiseerd door de verbouwing van station London Bridge, een netwerk upgrade en de

introductie van een nieuwe hoog frequente trein service door het centrum van London.

NS en ProRail hebben het Thameslink programma in London voornamelijk bezocht, omdat

Network Rail veel verder is met de implementatie van ATO. In Nederland staan de ontwikkelingen

omtrent ATO nog in de kinderschoenen, terwijl ze in London per december 2018 met ATO hopen

te rijden. De vragen vanuit NS en ProRail richten zich vooral op de veiligheidsoverwegingen,

acceptatie en implementatiestrategie.

Dit verslag is gebaseerd op de excursie van 15 maart 2017 naar Network Rail in London en geeft

informatie over het Thameslink probleem, de aanpak en de verwachte resultaten. Hoofdstuk 1

behandelt de theorie van het Thameslink vraagstuk. Hierin wordt uitgelegd wat de huidige situatie

is, de doelstellingen van het Thameslink programma zijn en hoe Network Rail deze doelstellingen

probeert te behalen. Hoofdstuk 2 bespreekt de veiligheid en operatie. Hierbij wordt een

onderscheid gemaakt tussen de kern (St. Pancras - Blackfriars) en het gebied buiten de kern. In

Hoofdstuk 3 wordt het plan en de aanpak geanalyseerd. Hierbij wordt gekeken naar de

uitvoering, de acceptatie binnen het opererend personeel, de voor- en nadelen van ATO over

ETCS en enkele bijzonderheden die niet direct met het Thameslink programma te maken

hebben. Het geheel sluit af met een samenvatting (Hoofdstuk 4). Vanwege de diverse

overtuigingen binnen de NS en ProRail kan iedereen hierna zijn/haar eigen conclusies trekken.

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1. Theorie Het theoretische deel van dit verslag bevat uitleg over het Thameslink programma. Allereerst zal

de doelstelling beknopt worden toegelicht (Hoofdstuk 1.1), gevolgd door een schets van de

huidige situatie (Hoofdstuk 1.2). Dit deel sluit af met de een oplossing die momenteel

geïmplementeerd wordt (Hoofdstuk 1.3).

1.1 Doelstelling Om de connectiviteit van London te verbeteren, heeft het ministerie van transport verschillende

doelen opgesteld en omgedoopt tot het Thameslink programma. Deze doelen zijn:

Verminder de overbelasting van de Thameslink en andere forensservices

Verminder de overbelasting van de Londense Metro

Verminder het aantal noodzakelijke overstappen tussen de hoofd- en nevenlijnen

Verbeter de bereikbaarheid van zuidoost Engeland

Faciliteer de passagier stroom van en naar St. Pancras

Samengevat komt dit neer op het realiseren van metro opvolgtijden op een regionaal spoor. Het

streven is per december 2018 24 treinen per uur te laten rijden over het spoor tussen Blackfriars

en St. Pancras (de kern), komende van verschillende richtingen in zowel het noorden als zuiden.

Figuur 1 geeft de gewenste situatie weer, waarbij de kern rood is.

Figuur 2 - Gewenste situatie Thameslink (24 treinen per uur)

1.2 Huidige situatie Figuur 2 toont het huidige Thameslink netwerk, waarbij de kern is aangegeven in het rood.

Momenteel rijden treinen van twee richtingen in het zuiden naar één richting in het noorden. Op

het kerngedeelte wordt zonder snelheidscontrole onder het Train Protection & Warning System

(TPWS) en Automatic Warning System (AWS) gereden. De maximaal toegelaten snelheid is daar

25 of 30 mph (40 of 50km/h).

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Figuur 2 - Huidige situatie Thameslink (16 treinen per uur)

Figuur 3 - Het capaciteit kritische wissel ten zuiden van Blackfriars

Het systeem in de kern bestaat uit twee sporen, waarbij de afstand tussen verschillende treinen

gebruikt wordt ter bepaling van seinlocaties, capaciteit, etc. Om de doorstroom te bevorderen

worden de seinen op de meeste stations in het midden van het perron geplaatst. De bottleneck

van het systeem ligt bij de wissel ten zuiden van Blackfriars (Blackfairs junction), waar de treinen

richting Elephant & Castle (southbound) kruisen met de treinen vanuit London Bridge

(northbound). De locatie van het wissel is in Figuur 2 aangegeven met een groene pijl en Figuur 3

geeft een gedetailleerder beeld van de bottleneck.

De capaciteit (technisch minimale opvolgtijd) wordt op de Engelse manier berekend, waarbij geldt

dat een geel sein groen wordt op het moment dat de trein zich 200 meter voor het betreffende

sein bevindt. Onder normale omstandigheden geeft AWS in deze situatie geen waarschuwing en

blijft deze de seinen monitoren. Buiten het waarschuwen van de machinist doet het systeem

weinig; zo legt het geen snelheidsbeperkingen op na het passeren van een geel sein. Let wel dat

AWS een noodremming inzet als de machinist niet aangeeft het gele sein te hebben gezien. Om

de veiligheid te garanderen zet TWPS zonder waarschuwing de noodrem in werking als de trein

door een rood sein dreigt te rijden. De tijd tussen de waarschuwing en daadwerkelijke

noodremming geven een soort rijtijdspeling, welke gebruikt kan worden om de dienstregeling te

herstellen in het geval dat TWPS ingrijpt. Momenteel is de maximale capaciteit over de kern 16

treinen per uur.

1.3 De oplossing In het verleden reden er verschillende treintypes en reizigersvervoerders over de Thameslink.

Recentelijk zijn al deze bedrijven samengevoegd tot de grootste rail franchise in het Verenigd

Koninkrijk, welke ongeveer 33% van het totale aantal passagiers vervoerd. Ter bevordering van

de homogeniteit van het treinverkeer over de Thameslink heeft het ministerie van transport 115

nieuwe class 700 treinen aangeschaft. De treinen bestaan uit 8 of 12 rijtuigen en kunnen niet

worden gecombineerd of gesplitst. De oplossing van de Thameslink zal dus worden uitgevoerd

door één infra manager, één vervoerder, één trein type en een algemene controle ruimte, welke

nu gebouwd wordt.

Zoals eerder vermeld is de gewenste capaciteit over de kern 24 treinen per uur. Met behulp van

simulaties heeft Network Rail bepaald dat het gebruik van ATO GoA2+ voor deze situatie

London Tower

79

noodzakelijk is. Zonder het gebruik van ATO is 20 treinen per uur het maximaal haalbare. Er zijn

twee belangrijke redenen dat ATO een hogere frequentie haalt. Ten eerste is ATO veel

consistenter, wat het mogelijk maakt de spreiding te reduceren. Dit leidt tot een

frequentieverhoging van 2 tot 2.5 treinen per uur. Meer over spreidingsreductie in Hoofdstuk 2.1.

De tweede reden van dit resultaat is de voor machinisten voorgeschreven decaleratie-eis omtrent

geel- en rood sein benaderingen (0.9 𝑚/𝑠2 voor ATO tegen 0.7𝑚/𝑠2 voor machinisten) [1]. Om

het verschil nog groter te maken, zal een machinist een decaleratie van 0.7𝑚/𝑠2 zelden halen.

Dit met name omdat het niet comfortabel is en erg abrupt voelt. Ter vergelijking; een voor

reizigers comfortabele remming in het Verenigd Koninkrijk is 0.4 𝑚/𝑠2 en de noodremming is

1.12 𝑚/𝑠2. Dit verschil resulteert uiteindelijk in een frequentieverschil van 1.5 tot 2 treinen per uur.

Om de implementatie te bevorderen heeft het ministerie van transport twee belangrijke besluiten

genomen. Om te beginnen heeft het ministerie 115 nieuwe treinen besteld en in haar beheer

genomen en ter beschikking gesteld aan de franchise. Ten tweede zijn alle bedrijven binnen de

franchise verplicht deze machines te gebruiken. Als een bedrijf de nieuwe treinen in gebruik

neemt, krijgt deze een bonus. Doen ze dit niet, dan is het bedrijf na december 2018 niet meer

welkom op de spoorwegen in de kern. Dit alles is contractueel vastgelegd.

Door de hoge frequentie waarmee de treinen door de Thameslink rijden resulteert iedere

afwijking in een verstopping. Ten behoeve van zulke situaties zijn er opstelterreinen en

keersporen aangelegd buiten de kern. Noodperrons, extra rails en wissels zijn aanwezig om naar

deze opstelterreinen en keersporen te leiden. Wanneer het probleem opgelost is, zal een

herstelschema de balans tussen de noord- en zuidsporen te herstellen. Om dit te realiseren wordt

gebruik gemaakt van een verhoogde doorstroom van maximaal 30 treinen per uur. In dit geval

zullen de treinen de maximale service brake gebruiken (0.9 𝑚/𝑠2).

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2. Veiligheid en operatie Dit deel van het verslag zal ingaan op de veiligheid en manier van operatie. In Hoofdstuk 2.1

wordt de situatie binnen het kerngebied (Blackfriars - St. Pancras) uitgelegd en Hoofdstuk 2.2

geeft aan wat er buiten de kern gebruikt wordt.

2.1 In de kern Sinds juli 2016 is ETCS geïnstalleerd binnen het kerngebied als overlay op de bestaande

beveiliging. Vanaf december 2018 zal elke trein op dit gebied onder ATO rijden. Ten behoeve van

de veiligheid controleert de machinist welke acties de trein uitvoert. Hiervoor worden de seinen

langs de baan gebruikt. Het is dan ook ten alle tijde mogelijk het ATO systeem te stoppen en

manueel verder te rijden. Bij manuele controle gaat de machinist rijden op TPWS/AWS. Hierdoor

is het niet nodig machinisten leertrajecten over ETCS zonder ATO te geven. Om de gelijke reden

zal er ook nooit een DAS voor ETCS ontwikkeld worden. Het kost relatief veel tijd om een trein

met ATO systeem te starten: 26 stappen moeten worden doorlopen. Op deze manier wordt de

machinist duidelijk gemaakt dat hij een ATO systeem opereert.

Omdat het kerngebied relatief klein is (7𝑘𝑚), worden alle voor ATO relevante parameters en de

routekaart opgeslagen in de trein. De dienstregeling en remcommandos worden door middel van

package 44 via GPS overgedragen van de ATP server naar de trein.

Naast ETCS zijn er extra stop- en markeerborden in de buurt van de detectiegrenzen. Treinen die

onder volledige supervisie (level 2) rijden kunnen een geel sein krijgen waarbij de movement

authority niet reikt tot het volgende rode sein. Dit stelt de trein in staat verder te rijden zonder dat

daarbij de veiligheid in het geding komt (spreidingsreductie). Figuur 3 illustreert dit concept.

Figuur 3 - Movement authority

Machinisten zijn op de hoogte van het eerder beschreven seinensysteem met verlengde

movement authority, om onnodige ingrepen te voorkomen. ATO negeert namelijk de door ETCS

opgelegde warning curve, wat resulteert in een hele andere rijstijl. Ook is het voor machinisten

die onder TPWS rijden niet toegestaan zonder toestemming door een oranje sein te rijden.

Naast de interventiemogelijkheden van de machinist wordt er nu ook gewerkt aan een procedure

die de verkeersleiding kan gebruiken om ATO uit te schakelen. Ook als dit gebeurt, gaat het

beveiligingssysteem van ETCS naar TPWS/AWS.

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Ten behoeve van interlocking op de Thameslink wordt InvenSys gebruikt. InvenSys is een

product van Siemens en vergelijkbaar met VPI (Vital Processor Interlocking) van Alstom.

Siemens levert ook het RBC (Radio Block Center) dat op de Thameslink gebruikt wordt. Deze

goedkope oplossing geeft de rijtoestemming door middel van GSM-R door aan de trein. Om deze

communicatie te bevorderen is het GSM-R bereik geüpdatet door middel van betere antennes en

systemen die dubbele signalen uitzenden.

2.2 Buiten de kern Buiten de kern wordt TPWS en AWS gebruikt ter beveiliging van de trein operatie. Vanwege de

homogene vloot is het combineren en splitsen van treinen niet aan de orde. Verder starten alle

services op TPWS/AWS gebied en rijdt alles in dit gebied hetzelfde als in de rest van het

Verenigd Koninkrijk.

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3. Analyse Binnen de analyse wordt gekeken naar de uitvoering (Hoofdstuk 3.1), de acceptatie binnen het

uitvoerend personeel (Hoofdstuk 3.2), de voor- en nadelen van ATO over ETCS (Hoofdstuk 3.3)

en enkele bijzonderheden die al dan niet direct met het Thameslink programma te maken hebben

(Hoofdstuk 3.4).

3.1 Uitvoering Omdat de treinoperatie binnen de kern automatisch wordt uitgevoerd, zullen standaard

processen veranderen. Dit hoofdstuk beschrijft de tijdens de excursie aan bod gekomen aspecten

en hoe deze veranderd zijn. Gladde sporen zijn de eerst besproken kwestie (Hoofdstuk 3.1.1),

gevolgd door het vertrekproces (Hoofdstuk 3.1.2) en geautomatiseerde route setting (Hoofdstuk

3.1.3). Het hoofdstuk sluit af met een stuk over bijscholing (Hoofdstuk 3.1.4) en het testen van de

ETCS overlay (Hoofdstuk 3.1.5).

3.1.1 Gladde sporen

Algemeen hebben ondergrondse sporen weinig tot geen last van gladheid. Vanwege de sterke

remming gebruikt door ATO, is het van cruciaal belang dat dit daadwerkelijk het geval is. Indien

de sporen wel glad zijn zal de trein zonder correctie het perron (deels) voorbij rijden. Vanwege de

hoge frequentie in de Thameslink is het terugrijden na een overshoot onmogelijk. De trein moet

dan verder rijden naar het volgende station.

Om dit probleem tegen te gaan zijn goede trein slip modellen nodig. De London underground

heeft over de laatste decennia haar trein slip model geoptimaliseerd. Deze kennis is dan ook

gebruikt ten behoeve van het Thameslink programma.

Tijdens operatie moet het materieel aangeven wat de huidige gladheidscondities zijn: ongeveer

hetzelfde als-, significant slechter dan-, significant beter dan aangegeven door de trackside. In

geval van cruising of uitrollen is het niet mogelijk de slipconditie te meten en geeft de trein aan

dat er niet gemeten wordt. Om dit protocol goed te laten werken moeten zowel de acceleratie,

decaleratie als precieze positie meegenomen worden. Het traffic management system (TMS)

gebruikt deze informatie om de gladheidsparameters up te daten en de gewenste acceleratie en

decaleratie van treinen in de buurt aan te passen. Verder correleert het TMS de situatie aan

lokale- en weercondities. Op deze manier kunnen in de toekomst betere voorspellingen gemaakt

worden.

ETCS heeft ook geïntegreerde slip modellen, maar deze modellen worden door gebrek aan

diepgang niet gebruikt.

3.1.2 Vertrekproces

Het is onduidelijk hoe men de veiligheid tijdens het instapproces op de Thameslink garandeert. Er

is geen conducteur of een afteller tot de vertrektijd. De enige tool die de machinist kan gebruiken

zijn de camera’s naast de deuren, waarmee de machinist kan monitoren of er nog iemand instapt.

In geval van drukte kan dit echter eenvoudig leiden tot gehaaste sluiting van deuren of vertraging.

Ter voorkoming van deze situaties is de class 700 trein zo ontworpen dat deze de tijdsduur van

de in- en uitstap proces verminderd. Net als bij metro’s zijn de deuren en gangpaden verbreed

en is er meer plaats om te staan. Hoe hier precies mee omgegaan zal worden ligt echter aan de

franchise; zij zijn immers verantwoordelijk voor het in- en uitstapproces.

3.1.3 Automatisch route setting

In het Verenigd Koninkrijk is algemeen geen automatic route setting (ARS) beschikbaar. Om

deze reden is voor de Thameslink een traffic management systeem (TMS) in ontwikkeling dat

83

zich bezighoudt met ARS. Bovendien moet met dit systeem de samenvoeging van

verkeersstromen aan het begin en einde van de kern (waaronder het capaciteitsknelpunt ten

zuiden van Blackfriars) worden geregeld. Dit TMS draait op een Hitachi systeem en informeert de

verkeersleiding buiten het kerngebied.

3.1.4 ATO bijscholing voor de machinist

Om met ATO te kunnen rijden is bijscholing noodzakelijk. Na komende zomer wordt gekeken of

het verstandig is machinisten vroeg om te scholen, of hier nog even mee te wachten. Door de

homogeniteitseis hoeven machinisten namelijk niet meer in de oudere treinen te rijden. Deze

worden namelijk uitgefaseerd. Dit geeft echter ook een probleem: het oude materieel kan niet

meer worden gebruikt in noodgevallen, omdat er geen gekwalificeerde machinisten meer zijn

(volgens de regels is een machinist na 6 maanden de kennis over ongebruikt materieel vergeten).

3.1.5 Het testen van ETCS

Voor het testen van de ETCS overlay is vier jaar uitgetrokken. Tests zijn gestart bij de

leverancier, gevolgd door het test- en integratie centrum. De derde testreeks is uitgevoerd op een

testspoor dat vergelijkbaar is met St. Pancras – Blackfriars, maar buiten de kern ligt (ENIF). De

laatste tests zijn ’s nachts uitgevoerd op de Thameslink zelf. Over de periode van deze tests is

ook het interlocking systeem gefinetuned om risico’s nog verder te verminderen. Ten behoeve

van operatieverbetering, hoopt het test- en integratiecentrum na de implementatie in december

2018 software updates voor trein en infrastructuur te mogen leveren. Of deze wens vervuld

wordt, is nog niet besproken en valt buiten het Thameslink programma.

3.2 Acceptatie door het uitvoerend personeel Weerstand tegen automatisering is niets nieuws. Omdat voor het gebruik van de Thameslink

ATO GoA2 geëist wordt, is het hier niet anders. Om stakingen en langdurige onderhandelingen

tegen te gaan, worden de volgende punten door de franchise benadrukt:

De machinist kan zich nu beter focussen op zijn hoofdtaak: de veiligheid.

Machinisten die de nieuwe technologie gebruiken krijgen een loonsverhoging.

De veiligheid wordt sterk verbeterd.

De machinist is niet langer verantwoordelijk voor het goed rijden van de trein binnen de

kern.

De implementatie van GoA3-4 duurt nog zo lang dat machinisten de komende decennia

nog noodzakelijk zullen zijn.

Mocht hun baan komen te vervallen, dan kunnen ze altijd nog op de stations werken.

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3.3 Voor- en nadelen van ATO over ETCS Tijdens de excursie in London is het programma van verschillende kanten belicht. Met behulp van

de gegeven presentaties is een lijst met voor- en nadelen opgesteld. Deze lijst is hieronder

gegeven:

3.1.1 Voordelen

Stabiel Europa-breed platform (hoewel de Europese standaard nog in ontwikkeling is en

nu al duidelijk is dat de implementatie bij Thameslink af zal wijken van die standaard)

Mogelijkheid tot behouden van het oude beveiligingssysteem als back-up

Systeem is uit te breiden

Nieuwe treinen werken in level STM (Specific Transmission Module)/NTC (Trein

uitgerust met ERTMS/ETCS rijdend op een lijn met een nationaal beveiligingssysteem)

vanaf de levering

Treinen zijn backward compatible en kunnen dus nog over het oude beveiligingssysteem

rijden.

ATO kiest de meest energiezuinige manier van operatie, gegeven de randvoorwaarden,

hoewel dit voor Thameslink geen drijfveer is. Met de lage snelheden die worden gereden

is de potentiele winst overigens beperkt.

Gedetailleerde ATO interface (Onboard routekaart, Event driven interface, Mogelijkheid

tot veranderen van dwell/run-time, modificeerbare remkracht.)

De mogelijkheid tot accuraat stoppen door balises

Kortere opvolgtijden zijn mogelijk door het negeren van de ETCS warning curve.

3.1.2 Nadelen

De verkeersleider heeft (nog) geen invloed op de trein als deze onder ATO rijdt.

ATO is onbekend in de meeste delen van het Verenigd Koninkrijk.

De functie van de machinist kan door verdere ontwikkelingen flink veranderen.

3.4 Opmerkingen Naast de situatiebeschrijving uit de vorige hoofdstukken zijn er nog enkele bijzonderheden al dan

niet van doen hebben met het nieuwe systeem, maar eerder iets zeggen over de manier van

werken in het Verenigd Koninkrijk. Deze bijzonderheden worden hieronder beschreven.

In het op de Thameslink wordt niet gekeken naar het energiegebruik (eco-driving) van de

treinen.

Het reizigers comfort is van ondergeschikt belang aan de haalbare capaciteit.

Het gebruik van ATO over TPWS/AWS is niet overwogen. Een belangrijk deel van de

capaciteitswinst komt uit de blokverdichting die onder ETCS relatief eenvoudig mogelijk

wordt.

Het gebruik van machinist advies systemen zijn niet overwogen.

De treinsimulator van Siemens gedraagt zich anders dan die van het test- en

integratiecentrum (welke de echte software gebruikt).

Ieder treintype heeft speciale positioneringsborden aan het perron.

Snelheidsrestricties voor wissels worden niet aangegeven met seinen, maar als bord met

een pijl.

Gezien de beschikbare ruimte is het onmogelijk op Blackfairs junction een fly-over of

dive-under te bouwen.

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4. Samenvatting Het Thameslink programma is een frequentie van 24 treinen per uur door het centrum van

London probeert te realiseren. Door alle personenvervoerders die over de Thameslink rijden

samen te voegen in één franchise en 115 nieuwe treinen aan te schaffen is het mogelijk al het

spoorverkeer in dit gebied te homogeniseren. Simulaties hebben aangetoond dat het, gegeven

de voorgeschreven rijstijl voor machinisten, onmogelijk is de gewenste capaciteit te behalen

zonder ATO GoA2+. Wanneer de situatie verstoord is, kan met behulp van bijgebouwde

opstelterreinen, keersporen, noodperrons extra rails & wissels een herstelprocedure in gang

gezet worden om de balans te herstellen. In dit geval zal de frequentie oplopen tot maximaal 30

treinen per uur.

Om de veiligheid te waarborgen kan op ieder moment overgeschakeld worden naar manuele

operatie over TPWS/AWS. Indien gebruik gemaakt wordt van ATO, staat de trein onder

supervisie van ETCS.

Ondanks de grote impact van automatisering wordt bijna geen rekening gehouden met de

machinist. Bij het gebruik van ATO krijgen ze een financiële beloning, maar als ze weigeren dit

systeem te gebruiken per december 2018, kunnen ze vertrekken.

Bibliografie

[1] Thameslink Programme: VISION Core Area ATO Study Report (2008); M. Baldry, M.

Turner and S. Blanchflower.

[2] Automatic Train Operation in GB (2016); European Union Agency for Railways ATO.

[3] ATO over ETCS: Achieving Metro Headways on the Mainline (2017), Jon Hayes.

[4] Signaller ATO training on Thameslink (2017); B.A. Graham.

[5] Thameslink – Desiro City & Signalling (2015); Siemens Mobility Division.

[6] Introduction to ETCS braking curves (2012); A. Hougardy, A. Chiappini, P. Guido.

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Appendix C – Matlab file for data analysis

This appendix contains the Matlab files used to process the data, determine the performance

indices punctuality and capacity (Section C.1), determine the safety criteria (Section C.2) and

provide a value for the minimal amount of runs for the simulation (Section C.3).

C.1 Data processing

% Data loader % FRISO simulatie data analysis clc; clear all; close all; tic

%% file properties

% % Realisated data % data_string{1} = 'Shl

BUP2016\Realisation\OTTfitted_PAB_20170503_195509.txt'; % data_string_name{1} = 'Safety: Realised Data'; % data_string_prop(1,:) = ; % data_save{1} = 'data_realised';

% Agent data_string{1} = 'Shl BUP2016\Agent\OTT_Disturbed_VaVo.dat'; data_string_name{1} = 'Safety: Agent'; data_string_prop(1,:) = 1792; data_save{1} = 'data_agent';

% Sportief data data_string{2} = 'Shl_New_Sim/Sportief/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{2} = 'Sportive driver'; data_string_prop(2,:) = 1841; data_save{2} = 'data_sport';

% IC Sportief 75% - SDAS 25% data_string{3} = 'Shl_New_Sim/Sportief75-

SDAS25/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{3} = '25% SDAS driver'; data_string_prop(3,:) = 1841; data_save{3} = 'data_25SD_75SP';

% 50% sportief - 50% SDAS data_string{4} = 'Shl_New_Sim/Sportief50-

SDAS50/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{4} = '50% SDAS driver'; data_string_prop(4,:) = 1841; data_save{4} = 'data_50SD_50SP';

% IC Sportief 25% - SDAS 75% data_string{5} = 'Shl_New_Sim/Sportief25-

SDAS75/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{5} = '75% SDAS driver'; data_string_prop(5,:) = 1831; data_save{5} = 'data_75SD_25SP';

% S-DAS data data_string{6} = 'Shl BUP2016/S_DAS/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{6} = '100% S-DAS';

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data_string_prop(6,:) = 1841; data_save{6} = 'data_S_DAS';

% C-DAS data data_string{7} = 'Shl BUP2016/C_DAS/OTT_Disturbed_VaVo.dat'; data_string_name{7} = 'C-DAS'; data_string_prop(7,:) = 1841; data_save{7} = 'data_C_DAS';

% TMS data data_string{8} = 'Shl BUP2016/Full TMS/OTT_Disturbed_FCFS.dat'; data_string_name{8} = 'Full TMS'; data_string_prop(8,:) = 1831; data_save{8} = 'data_TMS';

% IC SDAS - SPR Sportief data_string{9} = 'Shl_New_Sim/IC_SDAS-

SPR_sportief/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{9} = 'IC SDAS driver'; data_string_prop(9,:) = 1841; data_save{9} = 'data_IC_SD_SPR_SP';

% IC Sportief - SPR SDAS data_string{10} = 'Shl_New_Sim/IC_sportief-

SPR_SDAS/OTT_Disturbed_VaVo_Aim_at_plan.dat'; data_string_name{10} = 'SPR SDAS driver'; data_string_prop(10,:) = 1831; data_save{10} = 'data_IC_SP_SPR_SD';

for z = 1:1:max(size(data_string)) % Does the data file exist?

if exist(strcat(data_save{z},'.mat')) ~= 2 fprintf(strcat(data_save{z},'.mat not available, data will be

sorted \n'))

% Initialize run_size = data_string_prop(z,:); data_path = data_string{z};

% Open and read file file fid = fopen(data_path,'r'); datacell = textscan(fid, '%s'); fclose(fid);

cell_counter = max(size(datacell{1})); %cell_counter = data_string_prop(z,:);

% Empty train data matrices container = []; train_number =[]; replication_date = []; replication_date_table = []; t_arrival = []; t_arrival_table = []; t_departure = []; t_departure_table = [];

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delay_arrival = []; delay_departure = []; sub_area =[]; track = []; train_type = [];

%% Data sorting for i = 1:1:cell_counter container = [container; strsplit(cell2mat(datacell{1}(i)), ... {' ',';'},'CollapseDelimiters',true)]; train_number(i,1) = str2num(char(container(i,1))); replication_date = [replication_date;

cell2mat(container(i,2))]; t_arrival = [t_arrival; cell2mat(container(i,3))]; t_departure = [t_departure; cell2mat(container(i,4))]; delay_arrival(i,1) = str2num(char(container(i,5))); delay_departure(i,1) = str2num(char(container(i,6)));

if max(size(cell2mat(container(i,7)))) < 2 sub_area = [sub_area; '#####',cell2mat(container(i,7))]; elseif max(size(cell2mat(container(i,7)))) < 3 sub_area = [sub_area; '####',cell2mat(container(i,7))]; elseif max(size(cell2mat(container(i,7)))) < 4 sub_area = [sub_area; '###',cell2mat(container(i,7))]; elseif max(size(cell2mat(container(i,7)))) < 5 sub_area = [sub_area; '##',cell2mat(container(i,7))]; elseif max(size(cell2mat(container(i,7)))) < 6 sub_area = [sub_area; '#',cell2mat(container(i,7))]; else sub_area = [sub_area; cell2mat(container(i,7))]; end

if max(size(cell2mat(container(i,8)))) < 2 track = [track; '###',cell2mat(container(i,8))]; elseif max(size(cell2mat(container(i,8)))) < 3 track = [track; '##',cell2mat(container(i,8))]; elseif max(size(cell2mat(container(i,8)))) < 4 track = [track; '#',cell2mat(container(i,8))]; else track = [track; cell2mat(container(i,8))]; end

if str2num(char(container(i,10))) < 6 train_type = [train_type; '#IC']; elseif str2num(char(container(i,10))) < 11 train_type = [train_type; '#ST']; elseif str2num(char(container(i,10))) < 16 train_type = [train_type; '##G']; elseif str2num(char(container(i,10))) < 19 train_type = [train_type; 'HSL']; elseif str2num(char(container(i,10))) < 24 train_type = [train_type; 'SPR']; elseif str2num(char(container(i,10))) < 27 train_type = [train_type; '##S']; end end

% Convert time to a readable format for l = 1:1:cell_counter dump_a = strsplit(replication_date(l,:),{'-','.'}, ...

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'CollapseDelimiters',true); dump_b = strsplit(t_arrival(l,:),{'-','.'}, ... 'CollapseDelimiters',true); dump_c = strsplit(t_departure(l,:),{'-','.'},... 'CollapseDelimiters',true); for m = 1:1:max(size(dump_a)) replication_date_table(l,m) = str2num(char(dump_a(1,m))); end for m = 1:1:max(size(dump_b)) t_arrival_table(l,m) = str2num(char(dump_b(1,m))); t_departure_table(l,m) = str2num(char(dump_c(1,m))); end end

%% Data adaptation fprintf('Create train paths \n')

% Create empty variables train_route= {}; train_points = {}; cut_off = [];

% Determine unique train numbers and types [ unique_train_numbers ia ic ] = unique(train_number);

unique_train_types =

char(zeros(max(size(unique_train_numbers)),3)); for i = 1:1:max(size(ic)) unique_train_types(ic(i,1),:) = train_type(i,:); end

% Cut time into readable pieces % year month day hour minute second delay % altering arrival - departure .... - arrival - departure

% Include train_route_arrival for punctuality studies. for p = 1:1:max(size(unique_train_numbers)) iter = 1; for q = 1:1:cell_counter if unique_train_numbers(p,1) == train_number(q,1) if iter == 1; train_route{p} = [t_arrival_table(q,:), ... delay_arrival(q,1); t_departure_table(q,:), ... delay_departure(q,1)]; % train_route_arrival{p} = [t_arrival_table(q,:),

... % delay_arrival(q,1)]; iter = 0; else train_route{p} = [train_route{p}; ... t_arrival_table(q,:), delay_arrival(q,1); ... t_departure_table(q,:), delay_departure(q,1)];

% train_route_arrival{p} = [train_route_arrival{p};

... % t_arrival_table(q,:), delay_arrival(q,1)];

end

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end end end

% Define x-axis [stations] train_points = {}; for p = 1:1:max(size(unique_train_numbers)) iter = 1; for q = 1:1:run_size if unique_train_numbers(p,1) == train_number(q,1) if iter == 1; train_points{p} = [char(sub_area(q,:)); ... char(sub_area(q,:))]; iter = 0; else train_points{p} = [train_points{p}; ... char(sub_area(q,:)); char(sub_area(q,:))]; end

end end end

train_plot_path = []; for i = 1:1:max(size(train_points)) for j = 1:1:max(size(train_points{i})) if sum(train_points{i}(j,:) == '##LEDN') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == '###SSH') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == '##RVBR') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == '###NVP') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == '##SKBR') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == '##ASDZ') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == '##HFDO') == 6 train_plot_path(i,j) = 0; elseif sum(train_points{i}(j,:) == 'HSGHTZ') == 6 train_plot_path(i,j) = 1; elseif sum(train_points{i}(j,:) == 'HSGHTN') == 6 train_plot_path(i,j) = 2; elseif sum(train_points{i}(j,:) == 'HSHMDO') == 6 train_plot_path(i,j) = 3; elseif sum(train_points{i}(j,:) == 'HSHFDO') == 6 train_plot_path(i,j) = 4; elseif sum(train_points{i}(j,:) == '##HFDM') == 6 train_plot_path(i,j) = 5; elseif sum(train_points{i}(j,:) == '###HFD') == 6 train_plot_path(i,j) = 6; elseif sum(train_points{i}(j,:) == '###SHL') == 6 train_plot_path(i,j) = 7; elseif sum(train_points{i}(j,:) == '##ASRA') == 6 train_plot_path(i,j) = 8; elseif sum(train_points{i}(j,:) == '##ASDL') == 6 train_plot_path(i,j) = 9;

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elseif sum(train_points{i}(j,:) == '###AEG') == 6 train_plot_path(i,j) = 10; else train_points{i}(j,:) error('Station does not exist')

end end end

% Define y-axis [Time convertion] % Converge to minutes train_route_second = {}; for i = 1:1:max(size(train_route)) train_route_second_container = []; for j = 1:1:max(size(train_route{i})) train_route_second_container(j,:) = [train_route{i}(j,1)

... train_route{i}(j,2) train_route{i}(j,3) ... train_route{i}(j,4)*3600+train_route{i}(j,5)*60+... train_route{i}(j,6) train_route{i}(j,7)];

end train_route_second{i} = train_route_second_container; end

% Create average of simulation data train_route_second_averaged = {}; split_train_time_sec = {}; for i =1:1:max(size(train_route_second)) container_j = {}; matrix_counter = 1; matrix_counter_2 = 1; c_year = train_route_second{i}(1,1); c_month = train_route_second{i}(1,2); c_day = train_route_second{i}(1,3);

for j = 1:1:max(size(train_route_second{i})) if train_route_second{i}(j,1) > 1970 if (train_route_second{i}(j,2) ~= c_month &&

train_route_second{i}(j,3) ~= c_day) matrix_counter = matrix_counter + 1; c_day = train_route_second{i}(j,3); c_month = train_route_second{i}(j,2); matrix_counter_2 = 1; elseif train_route_second{i}(j,2) ~= c_month matrix_counter = matrix_counter + 1; c_month = train_route_second{i}(j,2); matrix_counter_2 = 1; elseif train_route_second{i}(j,3) ~= c_day matrix_counter = matrix_counter + 1; c_day = train_route_second{i}(j,3); matrix_counter_2 = 1; end container_j{matrix_counter}(matrix_counter_2,:) =

train_route_second{i}(j,:); matrix_counter_2 = matrix_counter_2+1; end

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end

split_train_time_sec{i} =

container_j(~cellfun('isempty',container_j)); end

% Create average time array average_train_time = {}; std_train_time = {};

% create STD and average for i = 1:1:max(size(split_train_time_sec))

time_container = []; delay_container = []; for j = 1:1:max(size(split_train_time_sec{i}{1})) for k = 1:1:max(size(split_train_time_sec{i})) time_container(j,k) = split_train_time_sec{i}{k}(j,4); delay_container(j,k) = split_train_time_sec{i}{k}(j,5); end end

average_delay = []; std_delay = []; average_runtime = []; std_runtime = [];

for l = 1:1:size(time_container,1)

sort_dc = sort(delay_container(l,:),'descend'); average_delay(l,1) =

mean(sort_dc(ceil(0.1*max(size(sort_dc))):max(size(sort_dc))-

ceil(0.1*max(size(sort_dc))))); std_delay(l,1) =

std(sort_dc(ceil(0.1*max(size(sort_dc))):max(size(sort_dc))-

ceil(0.1*max(size(sort_dc)))));

sort_tc = sort(time_container(l,:),'descend'); average_runtime(l,1) =

mean(sort_tc(ceil(0.1*max(size(sort_tc))):max(size(sort_tc))-

ceil(0.1*max(size(sort_tc))))); std_runtime(l,1) =

std(sort_tc(ceil(0.1*max(size(sort_tc))):max(size(sort_tc))-

ceil(0.1*max(size(sort_tc))))); end

average_train_time{i} = [average_runtime, average_delay]; std_train_time{i} = [std_runtime, std_delay]; end

%% Remove the train that ran during the 1st hour of the simulation iteration = 1; container_avg_time = {}; container_std_time = {}; container_nr = []; container_type = []; container_station = [];

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container_arrival_delay = [];

for i = 1:1:max(size(unique_train_numbers)) if min(average_train_time{i}(:,1)) > 3599 container_avg_time{iteration} = average_train_time{i}; container_std_time{iteration} = std_train_time{i}; container_nr(iteration,:) = unique_train_numbers(i,:); container_type(iteration,:) = unique_train_types(i,:); container_station(iteration,:) = train_plot_path(i,:); iteration = 1 + iteration; end end

average_train_time = container_avg_time; std_train_time = container_std_time; unique_train_numbers = container_nr; unique_train_types = container_type; train_plot_path = container_station;

% safe data fprintf('Data sorted\n') save(strcat(data_save{z},'.mat')); fprintf(strcat(data_save{z},'.mat created\n')) else % load data when it exists fprintf(strcat(data_save{z},'.mat available, data will be loaded

\n')) load(strcat(data_save{z},'.mat')) fprintf('Data loaded \n') end

% Variables to support further analysis SPR_counter = 1; IC_counter = 1; std_per_train_SPR = [];

std_per_train_IC = []; x_delay_container = []; %% Plot individual data std_per_train =[]; % figure(z) for i = 1:1:max(size(average_train_time)) plot_train_nr = unique_train_numbers(i,:); plot_train_type = unique_train_types(i,:);

% average realised time (arrival/departure) in minutes x_data = average_train_time{i}(:,1)/60;

% timetable time = realised time - delay [arrival/departure] x_tt = average_train_time{i}(:,1)/60-average_train_time{i}(:,2)/60;

% standard deviation of the arrival/departure times x_margin = std_train_time{i}(:,1)/60;

% timetable points [dienstregelpunten] y_data = train_plot_path(i,:);

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% arrival delay in minutes x_delay = average_train_time{i}(:,2)/60;

plot_x = []; plot_y = []; plot_x_margin = []; plot_x_tt = []; new_x_delay = [];

if x_data(1,1) ~= x_data(2,1) x_data = [x_data(1,1); x_data]; x_margin = [x_margin(1,1); x_margin]; x_tt = [x_tt(1,1); x_tt]; x_delay = [x_delay(1,1); x_delay]; end

if x_data(max(size(x_data)),1) ~= x_data(max(size(x_data))-1,1) x_data = [x_data; x_data(max(size(x_data)),1)]; x_margin = [x_margin; x_margin(max(size(x_margin)),1)]; x_tt = [x_tt; x_tt(max(size(x_tt)),1)]; x_delay = [x_delay; x_delay(length(x_delay),1)]; end

j = 1; for k= 1:1:max(size(y_data)) if y_data(1,k) ~= 0 plot_x(j,1) = x_data(k,1); plot_x_margin(j,1) = x_margin(k,1); plot_x_tt(j,1) = x_tt(k,1); plot_y(j,1) = y_data(1,k); new_x_delay(j,1) = x_delay(k,1); j = j+1; end end

% Plot figure fignum = z; if plot_train_type == '#IC' % figure(fignum) % hold on; % plot(plot_y,-plot_x_tt,'k-') % % h = fill([plot_y; flipud(plot_y)],[-plot_x+2*plot_x_margin;

... % flipud(-plot_x-2*plot_x_margin)],'b'); % hold off; % alpha(.1); % set(h,'EdgeColor','none') % hold on; % plot(plot_y,-plot_x-2*plot_x_margin,'b:') % plot(plot_y,-plot_x+2*plot_x_margin,'b:')

% Save IC data std_per_train_IC(IC_counter,:) = mean(x_margin); IC_counter = IC_counter+1;

elseif plot_train_type == 'SPR'

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% figure(fignum) % hold on; % plot(plot_y,-plot_x_tt,'k-') % % h = fill([plot_y; flipud(plot_y)],[-plot_x+2*plot_x_margin;

... % flipud(-plot_x-2*plot_x_margin)],'r'); % hold off; % alpha(.1); % set(h,'EdgeColor','none') % hold on; % plot(plot_y,-plot_x-2*plot_x_margin,'r:') % plot(plot_y,-plot_x+2*plot_x_margin,'r:')

% Save SPR data std_per_train_SPR(SPR_counter,:) = mean(x_margin); SPR_counter = SPR_counter+1;

else error('Train type not defined') end std_per_train(i,:) = mean(x_margin);

for r = 1:2:length(plot_y)-1 if plot_y(r,1) == 7 x_delay_container = [x_delay_container; new_x_delay(r,1)]; end end end

% ylabel 'Time [min]'; % xlabel 'Train station'; % xlim([0.5 10]); % ylim([-180 -120]); % % grid on; % % % % Total graph % % set(gca,'YTick',[-250 -200 -150 -100 -50] ); % % set(gca,'YTickLabel',{'250','200','150','100','50'}) % % % Hour graph % set(gca,'YTick',-180:10:-120 ); % set(gca,'YTickLabel',{'120','110','100','90','80','70','60'}) % % % Half hour graph % % set(gca,'YTick',-150:5:-120 ); % % set(gca,'YTickLabel',{'90','85','80','75','70','65','60'}) % % set(gca,'XTick',0.5:0.5:10 ); % set(gca,'XTickLabel',{' ','HSGHTZ',' ','HSGHTN',' ','HSHMDO', ... % ' ','HSHFDO',' ','HFDM',' ','HFD',' ','SHL',' ','ASRA',' ', ... % 'ASDL',' ','AEG'}) % % % set(findall(gcf,'type','text'),'FontSize',14); % h = findobj(gcf,'type','line'); % set(findall(gcf,'Type','axes'),'FontSize',14,... % 'LineWidth',2,'XColor','black','YColor','black')

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% % title(strcat(data_string_name{z},': TD plot 10-90percent'));

%% Devide in percentile data (punctuality)

delay_per_train = []; for i = 1:1:max(size(average_train_time)) delay_per_train(i,1) = mean(average_train_time{i}(:,2)); end

%% Combined plots and Analysis train_delay_sort{z} = delay_per_train;

average_std_per_train_3{z} = std_per_train; average_std_per_IC_3{z} = std_per_train_IC; average_std_per_SPR_3{z} = std_per_train_SPR;

%% Add all arrivals to a table arrival_delay{z} = x_delay_container;

end

%% Plot combined efforts

figure(97) hold on; save_unit_mean = []; save_unit_std = []; for i = 1:1:max(size(average_std_per_train_3))

% Cut highest and lowest 10% train_std = sort(average_std_per_train_3{i},'descend'); IC_std = sort(average_std_per_IC_3{i},'descend'); SPR_std = sort(average_std_per_SPR_3{i},'descend');

% Plot total standard deviation per method plot1 = plot([i-0.1 i-0.1],[0 ... mean(train_std(floor(0.1*length(train_std)):length(train_std)-... floor(0.1*length(train_std))))],'k*-'); save_unit_mean(i,1) =

mean(train_std(floor(0.1*length(train_std)):length(train_std)-... floor(0.1*length(train_std)))); save_unit_std(i,1) =

std(train_std(floor(0.1*length(train_std)):length(train_std)-... floor(0.1*length(train_std)))); % Plot IC deviation per method plot2 = plot([i i],[0 ... mean(IC_std(floor(0.1*length(IC_std)):length(IC_std)-... floor(0.1*length(IC_std))))],'b*-'); % Plot SPR deviation per method plot3 = plot([i+0.1 i+0.1],[0 ... mean(SPR_std(floor(0.1*length(SPR_std)):length(SPR_std)-... floor(0.1*length(SPR_std))))],'r*-');

97

end

xlim([0 max(size(average_std_per_train_3))+1]);

set(gca,'XTick', 0:1:max(size(average_std_per_train_3))+1 ); set(gca,'XTickLabel',{' ','Agent','Sportive','25% S-DAS','50% S-DAS','75%

S-DAS','S-DAS','C-DAS','TMS','IC S-DAS','SPR S-DAS',' '}) legend('Total STD','IC STD','Sprinter STD','Location','NorthWest')

set(findall(gcf,'type','text'),'FontSize',14); h = findobj(gcf,'type','line'); set(findall(gcf,'Type','axes'),'FontSize',14,... 'LineWidth',2,'XColor','black','YColor','black')

title 'Average standard deviation per operation type'; xlabel 'Way of operation'; ylabel 'Time [Minutes]' ylim([0.5 1.05]);

%% Time calculation time_elapsed = toc

%% Additional stuff

y_val = [0 0.99 3.53 6.43 8.40 10.82 19.79 6.06 0.82]; figure(98); plot(1:9,y_val,'r*-'); grid on; xlim([0 max(size(average_std_per_train_3))+1]);

set(gca,'XTick', 0:1:max(size(average_std_per_train_3))+1 ); set(gca,'XTickLabel',{' ','Sportive','25% S-DAS','50% S-DAS','75% S-

DAS','S-DAS','C-DAS','TMS','IC S-DAS','SPR S-DAS',' '}) ylim([0 22]); xlabel 'Way of operation'; ylabel 'Gain percentage'; title 'Gain percentage per operation type'; set(findall(gcf,'type','text'),'FontSize',14); h = findobj(gcf,'type','line'); set(findall(gcf,'Type','axes'),'FontSize',14,... 'LineWidth',2,'XColor','black','YColor','black') % % %% More stuff (punctuality) % % figure(99) % hold on; % plot_mode = ['k.-'; 'k.:'; 'k--'; 'r.-'; 'r.:'; 'r--'; 'b.-'; 'b.:'; 'b--

']; % % for w = 1:1:max(size(data_string)) % plot(10:10:90,60*quantile(arrival_delay{w},[0.1 0.2 0.3 0.4 0.5 0.6 0.7

0.8 0.9]),plot_mode(w,:)) % % end % % % legend('Sportive','25% S-DAS','50% S-DAS','75% S-DAS','S-DAS','C-

DAS','TMS','IC S-DAS','SPR S-DAS','Location','NorthWest')

98

% xlim([0 100]); % title 'Average delay per operation type'; % xlabel 'Percentile'; % ylabel 'Delay [Minutes]' % set(findall(gcf,'type','text'),'FontSize',14); % h = findobj(gcf,'type','line'); % set(findall(gcf,'Type','axes'),'FontSize',14,... % 'LineWidth',2,'XColor','black','YColor','black') % % % %% ANOVA test improvement % % save_unit_mean % % save_unit_std % % % Standard Error % % N = 50; % % SE_cont = []; % % DF_cont_1 = []; % % for i = 1:1:length(save_unit_mean) % % SE_cont(i,1) = (save_unit_std(i,1)^2)/N; % % DF_cont_1(i,1) = (save_unit_std(i,1)^2)/N; % % DF_cont_2(i,1) = (((save_unit_std(i,1)^2)/N)^2)/(N-1); % % end % % % % SE = []; % % DF = []; % % for i = 1:1:length(save_unit_mean)-1 % % SE(i,1) = sqrt(SE_cont(1)+SE_cont(i+1)); % % DF(i,1) =

(DF_cont_1(1)+DF_cont_1(i+1))^2/(DF_cont_2(1)+DF_cont_2(i+1)); % % end % % % % [h,p] =

ttest(average_std_per_train_3{1},average_std_per_train_3{4},'Alpha',0.05) % % % %% Save std per train % std_pt_container =[]; % for i = 1:1:length(average_std_per_train_3) % std_pt_container(:,i) = average_std_per_train_3{i}; % end % fid = fopen('std_per_train.dat', 'w'); % fwrite(fid, std_pt_container); % fclose(fid);

C.2 Safety criterion % Safety analysis v2 % FRISO simulatie data analysis clc; clear all; close all; tic

%% file properties

% Sportief data data_string{1} = 'Shl_New_Sim/Sportief/LogAnalyseVeiligheid.txt'; data_string_name{1} = 'Safety: Sportive driver'; data_save{1} = 'data_sport_safety';

% SDAS 25% data_string{2} = 'Shl_New_Sim/Sportief75-SDAS25/LogAnalyseVeiligheid.txt';

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data_string_name{2} = 'Safety: 75% Sportive driver - 25% SDAS driver'; data_save{2} = 'data_25SD_75SP_safety';

% SDASD 50% data_string{3} = 'Shl_New_Sim/Sportief50-SDAS50/LogAnalyseVeiligheid.txt'; data_string_name{3} = 'Safety: 50% Sportive driver - 50% SDAS driver'; data_save{3} = 'data_50SD_50SP_safety';

% SDAS 75% data_string{4} = 'Shl_New_Sim/Sportief25-SDAS75/LogAnalyseVeiligheid.txt'; data_string_name{4} = 'Safety: 25% Sportive driver - 75% SDAS driver'; data_save{4} = 'data_75SD_25SP_safety';

% S-DAS 100% data_string{5} = 'Shl BUP2016/S_DAS/LogAnalyseVeiligheid.txt'; data_string_name{5} = 'Safety: S-DAS'; data_save{5} = 'data_S_DAS_safety';

% C-DAS data data_string{6} = 'Shl BUP2016/C_DAS/LogAnalyseVeiligheid.txt'; data_string_name{6} = 'Safety: C-DAS'; data_save{6} = 'data_C_DAS_safety';

% TMS data data_string{7} = 'Shl BUP2016/Full TMS/LogAnalyseVeiligheid.txt'; data_string_name{7} = 'Safety: Full TMS'; data_save{7} = 'data_TMS_safety';

% IC SDAS data_string{8} = 'Shl_New_Sim/IC_SDAS-

SPR_sportief/LogAnalyseVeiligheid.txt'; data_string_name{8} = 'Safety: SPR Sportive driver - IC SDAS driver'; data_save{8} = 'data_IC_SD_SPR_SP_safety';

% SPR SDAS data_string{9} = 'Shl_New_Sim/IC_sportief-

SPR_SDAS/LogAnalyseVeiligheid.txt'; data_string_name{9} = 'Safety: IC Sportive driver - SPR SDAS driver'; data_save{9} = 'data_IC_SP_SPR_SD_safety';

for z = 1:1:max(size(data_string)) % Does the data file exist?

if exist(strcat(data_save{z},'.mat')) ~= 2 fprintf(strcat(data_save{z},'.mat not available, data will be

sorted \n'))

% Initialize data_path = data_string{z};

% Open and read file file fid = fopen(data_path,'r'); datacell = textscan(fid, '%s'); fclose(fid);

% Data size determination cell_counter = max(size(datacell{1}));

% Simulation time [h]

100

sim_time = 4;

% Empty data container container = [];

% Empty train data matrices simulation_number = []; time = []; error_type = [];

% Data sorting for i = 1:1:cell_counter container = [container; strsplit(cell2mat(datacell{1}(i)), ... {' ',';'},'CollapseDelimiters',false)]; simulation_number(i,1) = str2num(char(container(i,1)));

time_container = strsplit(cell2mat(container(i,2)), ... {' ',':'},'CollapseDelimiters',false);

time(i,1) = 3600*str2num(time_container{1}) + ... 60*str2num(time_container{2}) + str2num(time_container{3});

if max(size(cell2mat(container(i,3)))) > 20 if cell2mat(container(i,3)) == 'start_remming_voor_rood' error_type(i,1) = 2; else error('Value not recognized') end else if cell2mat(container(i,3)) == 'volgend_sein_rood' error_type(i,1) = 1; elseif cell2mat(container(i,3)) == 'gestopt_voor_rood' error_type(i,1) = 3; else error('Value not recognized') end end end

%% Proces data

% Remove first hour error_type = error_type(time > 3599); simulation_number = simulation_number(time > 3599); time = time(time > 3599);

unique_simulations = unique(simulation_number);

% Split in simulations for i = 1:1:max(size(unique(simulation_number))) counter = 1; error_container = []; for j = 1:1:max(size(error_type)) if unique_simulations(i,1) == simulation_number(j,1) error_container(counter,:) = error_type(j,1); counter = counter + 1; end

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end error_split{i} = error_container; end

next_red = []; break_red = []; stop_red = [];

% Count problems for i = 1:1:max(size(error_split)) next_red(i,:) = max(size(error_split{i}(error_split{i}(:) ==

1)))/sim_time; break_red(i,:) = max(size(error_split{i}(error_split{i}(:) ==

2)))/sim_time; stop_red(i,:) = max(size(error_split{i}(error_split{i}(:) ==

3)))/sim_time; end

% safe data fprintf('Data sorted\n') save(strcat(data_save{z},'.mat')); fprintf(strcat(data_save{z},'.mat created\n')) else % load data when it exists fprintf(strcat(data_save{z},'.mat vailable, data will be loaded

\n')) load(strcat(data_save{z},'.mat')) fprintf('Data loaded \n') end

next_red_2{z} = next_red; break_red_2{z} = break_red; stop_red_2{z} = stop_red; %std_next_red{z} = std(next_red); %std_break_red{z} = std(break_red); %std_stop_red{z} = std(stop_red);

% Prepare plot data

% figure(z) % hold on; % h = fill([1 3 3

1],[mean(next_red(isoutlier(next_red)<1))+2*std(next_red(isoutlier(next_red

)<1)),... %

mean(stop_red(isoutlier(stop_red)<1))+2*std(stop_red(isoutlier(stop_red)<1)

), mean(stop_red(isoutlier(stop_red)<1))-... % 2*std(stop_red(isoutlier(stop_red)<1)),

mean(next_red(isoutlier(next_red)<1))-

2*std(next_red(isoutlier(next_red)<1))],'r'); % hold off; % alpha(.1); % set(h,'EdgeColor','none') % hold on; % plot([1 3],[mean(next_red(isoutlier(next_red)<1))

mean(stop_red(isoutlier(stop_red)<1))],'r'); %

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% plot([1

3],[mean(next_red(isoutlier(next_red)<1))+2*std(next_red(isoutlier(next_red

)<1))... %

mean(stop_red(isoutlier(stop_red)<1))+2*std(stop_red(isoutlier(stop_red)<1)

)],'r:'); % plot([1 3],[mean(next_red(isoutlier(next_red)<1))-

2*std(next_red(isoutlier(next_red)<1))... % mean(stop_red(isoutlier(stop_red)<1))-

2*std(stop_red(isoutlier(stop_red)<1))],'r:'); % % ylabel 'Amount per hour'; % title(strcat(data_string_name{z},': Red signal approaches')); % % xlim([0.5 3.5]) % ylim([0 90]) % set(gca,'XTick', 0.5:0.5:3.5); % set(gca,'XTickLabel',{' ','Red signal approach',' ',' ',' ', ... % 'Stop for red signal', ' '}) % % set(findall(gcf,'type','text'),'FontSize',14); % h = findobj(gcf,'type','line'); % set(findall(gcf,'Type','axes'),'FontSize',14,... % 'LineWidth',2,'XColor','black','YColor','black'); % %

end

%% Plot break red

figure(98) hold on; plot_mode = ['k.-'; 'k.:'; 'k--'; 'r.-'; 'r.:'; 'r--'; 'b.-'; 'b.:'; 'b--

'];

for w = 1:1:max(size(data_string)) hold on; plot([w w],[0 mean(next_red_2{w})],'k*:'); errorbar(w,mean(next_red_2{w}),std(next_red_2{w}),'r') errorbar(w,mean(next_red_2{w}),2*std(next_red_2{w}),'b'); errorbar(w,mean(next_red_2{w}),std(next_red_2{w}),'r')

plot([w+0.2 w+0.2],[0 mean(stop_red_2{w})],'k*:'); errorbar(w+0.2,mean(stop_red_2{w}),std(stop_red_2{w}),'r'); errorbar(w+0.2,mean(stop_red_2{w}),2*std(stop_red_2{w}),'b'); errorbar(w+0.2,mean(stop_red_2{w}),std(stop_red_2{w}),'r');

end

ylabel 'Amount per hour'; title 'Red signal approaches';

%legend('Sportive','25% S-DAS','50% S-DAS','75% S-DAS','S-DAS','C-

DAS','TMS','IC S-DAS','SPR S-DAS','Location','SouthWest')

103

legend('Average','1x Std','2x Std','Location','NorthEast');

xlim([0 11]) ylim([0 85]) set(gca,'XTick', 0:1:11); set(gca,'XTickLabel',{' ','Agent','Sportive','25% S-DAS','50% S-DAS','75%

S-DAS','S-DAS','C-DAS','TMS','IC S-DAS','SPR S-DAS', ' '})

set(findall(gcf,'type','text'),'FontSize',14); h = findobj(gcf,'type','line'); set(findall(gcf,'Type','axes'),'FontSize',14,... 'LineWidth',2,'XColor','black','YColor','black')

%ylabel 'Standard Deviation';

%% Confidence interval determinator [99%] figure(99) hold on;

for w = 1:1:max(size(data_string)) SEM = std(next_red_2{w})/sqrt(length(next_red_2{w})); % Standard

Error ts = tinv([0.005 0.995],length(next_red_2{w})-1); % T-Score CI = ts*SEM; % Confidence

Intervals

p1 = plot([w-0.1 w-0.1],[0 mean(next_red_2{w})],'k:'); p3 = errorbar(w-0.1,mean(next_red_2{w}),CI(1),CI(2),'r');

SEM = std(stop_red_2{w})/sqrt(length(stop_red_2{w})); % Standard

Error ts = tinv([0.005 0.995],length(stop_red_2{w})-1); % T-Score CI = ts*SEM; % Confidence

Intervals

p2 = plot([w+0.1 w+0.1],[0 mean(stop_red_2{w})],'k--'); errorbar(w+0.1,mean(stop_red_2{w}),CI(1),CI(2),'r'); end

ylabel 'Amount per hour'; title 'Safety data'; legend([p1 p3],'First','Third') legend([p1 p2 p3],'Red signal approach','Red signal stops','99% Confidence

interval','Location','NorthEast');

xlim([0 10]) ylim([0 85]) set(gca,'XTick', 0:1:10); set(gca,'XTickLabel',{' ','Sportive','25% S-DAS','50% S-DAS','75% S-

DAS','S-DAS','C-DAS','TMS','IC S-DAS','SPR S-DAS', ' '})

set(findall(gcf,'type','text'),'FontSize',14); h = findobj(gcf,'type','line'); set(findall(gcf,'Type','axes'),'FontSize',14,... 'LineWidth',2,'XColor','black','YColor','black')

104

%% Time calculation time_elapsed = toc

% %% Data manipulation % % break_red_list = []; % next_red_list = []; % stop_red_list = []; % % for i = 1:1:9 % next_red_list(i,:) = next_red_2{i}'; % break_red_list(i,:) = break_red_2{i}'; % stop_red_list(i,:) = stop_red_2{i}'; % end

C.3 Amount of runs % FRISO simulatie data analysis % Determine the required amount of experiments clc; close all; clear all;

%% Load sportive file fprintf('data_sport.mat available, data will be loaded \n') load('data_sport.mat') fprintf('Data loaded \n')

%% Adapt data % Split train sec iteration = 1; container_split_sec = {}; for i = 1:1:length(split_train_time_sec) if min(split_train_time_sec{i}{1}(:,4)) > 3599 container_split_sec{iteration} = split_train_time_sec{i}; iteration = iteration + 1; end end % Split train sec split_train_time_sec = container_split_sec;

%% Sample size check % error allowance alpha = 0.005; epsilon = 0.01; sim_number = 50;

% Per control-point % For each train arrival and departure, determine the mean and standard % deviation over 50 experiments fprintf('Per point\n'); sample_check = {}; for i = 1:1:length(split_train_time_sec) sample_check_cont = []; for j = 1:1:length(split_train_time_sec{i}{1}) sample_container = [];

105

for k = 1:1:length(split_train_time_sec{i}) sample_container(k,1) = split_train_time_sec{i}{k}(j,4); end sample_check_cont(j,:) = [mean(sample_container)

std(sample_container)]; end sample_check{i} = sample_check_cont; end

% mimimal amount of required simulations R = {}; for i = 1:1:length(sample_check) R_container = []; for j = 1:1:length(sample_check{i}) R_container(j,1) = (tinv(1-

2*alpha,50)*sample_check{i}(j,2)/(epsilon*sample_check{i}(j,1)))^2; end R{i} = R_container; end

R_sum = []; R_amount = []; R_max_container =[]; for i = 1:1:length(R) R_sum(i,1) = sum(R{i} > sim_number); R_amount(i,1) = max(size(R{i})); R_max_container(i,1) = max(R{i}); end

total_amount = sum(R_amount) Larger_than_sim_number = sum(R_sum) R_max = ceil(max(R_max_container))

% Per train fprintf('Per train \n') sample_check = {}; for i = 1:1:length(split_train_time_sec) sample_container = []; for k = 1:1:length(split_train_time_sec{i}) sample_container(k,1) = sum(split_train_time_sec{i}{k}(:,4)); end sample_check{i} = [mean(sample_container) std(sample_container)]; end

R = []; for j = 1:1:length(sample_check) R(j,1) = (tinv(1-

2*alpha,50)*sample_check{j}(1,2)/(epsilon*sample_check{j}(1,1)))^2; end

R_sum = []; R_amount = []; for i = 1:1:length(R) R_sum(i,1) = sum(R(i,1) > sim_number); R_amount(i,1) = max(size(R(i,1))); end total_amount= sum(R_amount) Larger_than_sim_number = sum(R_sum) R_max = ceil(max(R))

106

Appendix D – Data logging

D.1 OTT Logging

The OTT logging contains information regarding the realised and planned train schedules and

consists of several parameters determined per train per sub-area, separated by a semicolon.

Train number

The train numbers are built of 7 or more digits. Here, the last three digits identify the specific train

trip/instance. A train series can have several instances during one day. The 4th and 5th digit from

the right indicate the sub-series number, while the 6th digit from the right indicates the direction.

Here 0 stands for H (forwards; NL: heen) and 1 for T (backwards; NL: terug). The remainder of

the train number is the number of the train series. It is possible spare zeros are added on the left.

Replication date

A fictional date on which the replication is run. Every instance of the simulation is run on a

different date. The date is formatted as ‘yyyy - mm – dd’.

Realised arrival time

The realised arrival time, which is calculated as plannedarrivaltime + arrivaldelay. The arrival time

is written as ‘yyyy -mm-dd-hh:mm:ss’. In case a certain train has no arrival in the specific sub-

area, the realised arrival time is set to ‘1970 - 01 - 01 - 00:00:00’. This occurs if the train starts

service at the specific sub-area.

Realized departure time

The realized departure time, which is calculated as planneddeparturetime+departuredelay. The

departure time is written as ‘yyyy - mm - dd - hh:mm:ss’. In case a certain train has no departure

from the specific sub-area, the realized departure time is set to ‘1970 – 01- 01 - 00:00:00’. This

occurs if the train terminates service at the specific sub-area.

Arrival delay

The arrival delay given in seconds. A negative value means the train arrived too early, while an

arrival delay of 999999 indicates that the train had no arrival at this sub-area.

Departure delay

The departure delay given in seconds. A negative value means the train departed too early, while

a departure delay of 999999 indicates that the train had no departure at this sub-area.

Sub-area name

String that contains the sub area abbreviation.

Track name

String that contains the track name. In case track name contains a number, it is considered to be

an open track (the central train control cannot influence these tracks).

Train type

107

The train type given as an integer. Here the numbers 1 to 5 indicate an intercity, the numbers 6 to

10 a fast train (no longer in use), the numbers 11 to 15 a freight train, the numbers 16 to 18 a

high speed train, the numbers 19 to 23 a regional train and the numbers 24 to 26 a slow train (no

longer in use).

In addition, the logging file contains three empty data fields. These are not included in the list for

clarity reasons.

D.2 Safety logging The safety logging contains information regarding red signals. FRISO provides to following

information, divided by a semi-colon.

Replication number

The replication number is an integer that logs the number of the occurrence within one simulation

run.

Time The time in the simulation during which the red signal encounter occurred. The time is provided

as 0hh : mm : ss0.

Situation The severity of the signal encounter. Possible options are next signal red, start braking for red

and stopped before red. In all cases, planned stops are excluded.

Train series The number of the train series that encountered the red signal.

Signal name The name of the encountered red signal

Signal route The name of the route containing the signal. The value is unknown, in case the signal is on an

open track.

Sub-area name The name of the sub-area containing the signal

Area name The name of the sub-area containing the signal

Operating type The process control type. Possible values are VaVo and FCFS. VaVo is listed if the operating

train order is maintained. FCFS, first come first serve, is listed and applied otherwise.

Name of the current route The name of the train route containing the signal

Distance The distance between subsequent trains in meters, when encountering a red signal. Zero is used

in case this distance is unknown.

108

Cause The cause provides the train that causes the red signal encounter.

Description of the situation Description of the encountered situation. Possible values are claimed (infrastructure is claimed

but not yet occupied by another train), head-head, head-tail, crossing, unknown and VaVo

(another train is planned first).

109

Appendix E – TD plots for validation This appendix contains the TD-plots used for the validation of the simulation implementation.

Figure 4 – Agent

Figure 5 - S-DAS

110

Figure 6 - C-DAS

Figure 7 - TMS

111

Appendix F – TD plots This appendix contains the TD-plots for all different driver profiles.

Figure 8 - Sportive driver

Figure 9 - 25% S-DAS based ATO

112

Figure 10 - 50% S-DAS based ATO

Figure 11 - 75% S-DAS based ATO

113

Figure 12 - 100% S-DAS based ATO

Figure 13 - IC S-DAS based ATO

114

Figure 14 - Sprinter S-DAS based ATO

Figure 15 - CDAS based ATO

115

Figure 16 - TMS based ATO