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IIIIMPACT OF PARKING MANEUVERS ON MPACT OF PARKING MANEUVERS ON MPACT OF PARKING MANEUVERS ON MPACT OF PARKING MANEUVERS ON

SPACE MEAN SPEED AND AVERAGE SPACE MEAN SPEED AND AVERAGE SPACE MEAN SPEED AND AVERAGE SPACE MEAN SPEED AND AVERAGE

TRAVEL TIMETRAVEL TIMETRAVEL TIMETRAVEL TIME

Ermias Tesfaye Adula

KTH, May 2011

Impact of Parking Maneuvers on Space Mean Speed

And Average Travel Time

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3

Abstract

Parking maneuver is one among the numerous factors which affect the traffic movement. In

this thesis work the impact of parking maneuvers and double parking on the traffic

performance of urban street segments is studied. In order to analyze the traffic

performance, the impact on the travel time and space mean speed is studied. Two

approaches are used for the study; the Micro analysis and Macro analysis. In the Micro

analysis the study is not time bounded. The impact of individual vehicles is studied against

the event of interest. These events include inbound parking maneuvers, outbound parking

maneuvers, double parking and the load/unload activities. On the other hand for the Macro

study the events in a five minute interval are studied for eight and a half hour for each site.

Each five minute interval is taken as a single data input for the regression analysis. To study

the impact on travel time speed trajectory is used and to study the space mean speed

regression analysis is used. According to this study the parking maneuvers give rise to a

speed reduction of almost 2 Km/hr and double parking 7km/hr. Similarly, the load/unload

maneuvers cause a speed reduction of 3Km/hr.

Normally two basic tasks are included in this thesis work; data reduction and data analysis.

The raw data for this thesis work is a video recording of 8 and half hours for four different

spots. Regression analysis and speed trajectory are the main analysis approaches employed

for the data analysis. Speed trajectory is used for the Micro analysis and regression analysis

is used for the Macro analysis.



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5

Acknowledgments

This thesis project is part of the Parking project which is carried out at the traffic and

logistics department of KTH. This Parking study, which is focusing on the selected streets in

the central areas of Stockholm, is carried out by a team of professionals lead by Professor

Karl-lennart Bång.

With all his grace thanks to the Almighty God I finally made it through with the

unexplainable collaboration and assistance of numerous people.

First, I would like to thank and appreciate Professor Karl-lennart Bång for his best guidance

and wisdom sharing. As my supervisor and chairman of the project, his guidance put on the

right alignment to accomplish my work.

Secondly, I would like to thank the project team members Ary-Pezo Silvano (Phd.

Candidate), Dr. Albania Nissan and Dr. Azhar Al-Mudhaffar for their continuous assessment.

They were so perfect in following every move I did. My special thanks go to Professor Harris

Koutsopoulos for his time and best ideas in solving my major problems.

Finally, I would like to thank my wife Tsedey Yeshitila Belayneh for all the caring and

support. Lastly, I would like to mention my family back home. This paper work is dedicated

to my late Dad, whose dream drove me extra miles in my academic life.



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Table of Contents

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

1.1 Background ............................................................................................................................. 9

1.2 Parking Definition.................................................................................................................... 9

1.3 Objective ............................................................................................................................... 10

1.4 Scope and Limitation............................................................................................................. 10

2 Literature Review .......................................................................................................................... 11

3 Methodology ................................................................................................................................. 17

The data used for this project is a video recording which lasts for about eight and half hour for

each site. This data was collected in September 2009 by the division. From this raw data intensive

observation was made to collect all the necessary information. ..................................................... 17

3.1 Micro Study ........................................................................................................................... 17

3.1.1 Free Flow Speed ............................................................................................................ 17

3.1.2 Parking Maneuver ......................................................................................................... 17

3.2 The Macro Study ................................................................................................................... 19

3.2.1 Data Analysis/Processing .............................................................................................. 20

3.2.2 Regression Analysis ....................................................................................................... 21

4 Description of Case Study (Data Analysis) .................................................................................... 27

4.1 Case Study One ........................................................................................................................... 27

4.2 Case study Two ..................................................................................................................... 32

4.3 Case study Three ................................................................................................................... 34

5 Results and Discussion .................................................................................................................. 36

5.1 Micro Analysis ....................................................................................................................... 36

In the Micro analysis the data reduction is not done in a time bound manner. Some of the

interesting events of the same kind are categorized together to see their impact. The

interference of some other disturbing factors than the events of interest was more challenging

to exclude. ..................................................................................................................................... 36

5.1.1 Case Study One ............................................................................................................. 36

5.1.2 Case Study Two ............................................................................................................. 46

5.1.3 Case Study Three ........................................................................................................... 54

5.2 Macro Analysis ...................................................................................................................... 62

5.2.1 Evaluation of the Regression Analysis .......................................................................... 62



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5.2.2 Evaluation of the Initial Hypotheses ............................................................................. 63

6 Discussion ...................................................................................................................................... 66

7 Conclusions ................................................................................................................................... 67

8 Further Study ................................................................................................................................ 68

9 Bibliography .................................................................................................................................. 69



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1 Introduction 1.1 Background

With the fast growth of automobile use in the early 19th

century decision makers and urban

planners visualize the problems associated with the availability of proper parking space. For

this reason it has become one of the major parts of a modern urban set up. In previous

times parking related problems appear to be seen in the city centers and in areas with

higher public movements. But, nowadays it is a concern throughout the urban area, even

with those having moderate public activities. Parking activity gives the cities and suburbs the

picture that they hold; it has impact on the traffic congestion, traffic operation and also

determines the reliability of transit systems. The presence of parking space also puts a great

deal of influence on the mode choice and route selection of travelers. On-street parking in

situations without proper parking space could increase the accident risks and also could

obstruct emergency vehicles. Additionally, in the mid 1930’s it was realized that it will be a

major source of revenue (Childs, 1999).

Many attempts were done to implement and propose different measures in order to create

a better living environment and make people’s interaction so smooth. A well studied and

designed parking policy results in an efficient transport system, lower emissions and

inclusive urban layout (Marsden, 2006). Besides, accessibility and parking suitability are

among the most decisive factors that affect shoppers’ destination choices (Innes D.et al,

1990). Parking policy is a vital issue for economy, sustainability and suitability of cities to live

in.

In Stockholm different policies have been implemented to improve the traffic condition in

the inner city. One of the activities which affect the traffic condition in the innercity is the

parking activity. For this purpose this study is carried out to evaluate the impact of the

parking activities in the traffic performance of the streets in Stockholm.

1.2 Parking Definition

Usually vehicles have an origin and destination. Typically private cars usually have their

origin in residential areas and destination in central city areas. Those cars have to be parked

in the city areas which are flocked with cars. One point that we always have to bear in mind

is that cars are parked for most of their life time.

Various types of designs and facilities have been introduced to park cars. Literally, parking

places can be categorized as:

- Off-street parking indoors

- Off-street parking outdoors

- On-street parking (with fee or no fee)

Off-street parking indoors



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This is a sort of facility which is designed to store cars out of the road network. They can

be in the form of a basement which is built underground, level ground or a multi-story

building is allocated for it. Mostly they are located in central business areas where there

is a high demand for parking and less available space.

Off-street parking outdoors

These are parking lots at the ground level. They are quite common in office areas,

residential areas, and big shopping malls or supermarkets. Despite the fact that they

appear at the ground level they do not have a direct interaction with the traffic stream.

They have typical layout design which can accommodate as many vehicles as possible.

They also have enough drive way and medians.

On-street parking outdoors

This in most literatures is called curb side parking. It is a parking on the roadside

bordering the traffic stream. Mostly these types of parking activities happen to be free

of charge in residential areas. On the other hand in central areas there are parking

meters for the drivers to pay parking fees.

On-street parking outdoors is the type of parking that will be studied in this thesis work.

1.3 Objective

The aim of this study is to analyze the impact of parking maneuvers and double parked

vehicles on the traffic performance of urban roads. Specifically, this thesis work studies the

impact of those mentioned events on the speed and travel time of an urban street segment

in Stockholm. For this purpose two approaches will be employed; Macro analysis and Micro

analysis. In the Micro analysis the impact of individual maneuvers is studied whereas in the

Macro analysis the impact of the events is studied in an aggregated approach. In doing so,

the interference of other types of maneuvers or some other events will be excluded.

1.4 Scope and Limitation

At the start of this project the impact of bicyclists and pedestrians was studied. But in the

preliminary assessment these parameters were not found significant. The bicyclists use their

own lane unless obstructed and the number of pedestrians crossing not on the zebra cross

is very few. For those reasons the two variables are excluded from the study. The

independent variables used in the Macro analysis are flow, maneuvers, double parking,

load/unload and number of lanes. These variables are found significant.

The other limitation is the impact of aggregation and averaging. Due to some constraints the

time segment could not be narrower than five minutes in the Macro study the analysis.

When the events in a five minute interval are average, some events happen to be covered

up and their impact will not be seen more significantly than those without any event.

Usually when events occur like with the red traffic light, bunching of vehicles happen. In this

study the bunching vehicles are not analyzed.



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2 Literature Review There are lots of literatures about parking issues which are related to parking behavior,

parking policies, economic analysis of garages, optimization of garages, etc. However, there

are very few literatures regarding the influences of on-street parking on traffic performance,

traffic safety, logistic (Load-Unload), accessibility and the environment.

The main objective of this literature review is to revise the papers on how on-street parking

activities influence the speed of adjacent traffic stream and capacity of the road where they

are situated. Normally there is a direct relation between speed and capacity in the intercity

roads; as the speed reduces so does the capacity. It has been advocated that on-street

parking affects the capacity and speed of the streets. An intensive search on different

sources on this subject matter is summarized in the following.

In literatures it is stated that on-street parking reduces the capacity for various reasons. To

mention some; the on-street parking lane could have been dedicated to the through traffic

and it also reduces the capacity as it reduces the speed of the vehicles due to the side

friction. This is also called roadside characteristic and is demonstrated by the models

elaborated below.

Bång,_K._L. (1995) developed speed-flow relationships and road capacity (�) in the

Indonesian Highway Capacity Manual. On-street parking was taken in to account in the

estimation process. Capacity was related to explanatory variables such as road function,

roadside friction and road width. For the roadside friction in the urban streets, on-street

parking was considered with the number of parking maneuvers occurring in the road

segment (��/�/��). In order to estimate the parameters of the models linear regression

was applied. The capacity model is:

� � � �� 1.1

Where

� = Actual capacity

� = Ideal capacity

�� = Road width adjustment factor

�� = Directional adjustment factor

�� = Road friction adjustment factor

Different road characteristics were considered in the roadside adjustment factor(�� );

these include pedestrians(Ped), on-street parking maneuvers(PSV), number of bus stops and

slow moving vehicles (SMV) with different weights. The following equation gives an



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empirical relationship which tells the dependence of road frictional adjustment factor on

the factors mentioned above:

FCsf=Ped*0.6+PSV*0.8+SMV*0.4

According to Bång (1995), the roadside activities significantly affect the speed-flow

relationship of the road links. This will result in the speed and capacity reductions. In the

graph in Figure 2.1 below it can be seen how speed and capacity are reduced due to side

friction events.

Figure 2.1: The relationship of speed and capacity with side friction,

Source: Bång, K.L (1995)

Another approach was made by Highway Capacity Manual (HCM 2010-Chapter 17). For

urban street segments it considers geometry, traffic and control characteristics to compute

urban street segments performance and Level of Service (LOS). Four modes are considered

in the methodology: pedestrian, automobile, bicyclist and transit. In the computation of LOS

for pedestrian and bicyclist modes on-street parking is considered directly; yet the HCM

2010 does not consider on-street parking for automobile mode. It is left to the analyst to

complement the analysis adding typical methodology if available to compute it’s influence

on the traffic performance.



13

Ibeas-Portilla, A._et_al (2009) evaluated traffic performance due to on-street parking with a

different approach. To quantify the influence of parking maneuvers and bad parked cars on

average link journey times as a function of the duration of the events and flow the authors

applied M/M/∞ queuing model. To calibrate the model data was collected from the city

Santander in Spain. Validation was done with micro simulation (AIMSUM). Parking

maneuvers and badly parked cars are introduced as events where the lanes do not work or

work at a lower normal rate of service. Parking maneuvers are introduced as high frequency

short duration event (10, 20 and 30 maneuvers per hour) and badly parked cars are

introduced as time events (15 min, 30 min, 45 min and 60 min). The results show an

increase in average journey time up to by 15, 24 and 39% for 10, 20 and 30 maneuvers

respectively; and a reduction in capacity of 6, 10 and 16% for maneuvers of 10, 20 and 30

respectively. Similarly the journey times increase by 57 and 107% for badly parked vehicles

for 15 and 30 min respectively; and capacity reduce by 13 and 27% respectively. When the

event lasted for 60 min the capacity reduction reached 55%.

Studies illustrate that speed reduces with the presence and amount of on-street parking for

urban streets (Shoup 2006, Childs 1999). All of the studies carried out linear regression

analysis to find the average speed on the streets. They took in to account road

characteristics as predictors of the dependant variable (Space mean speed). In all the

studies the presence of on-street parking was found to be an important factor to reduce the

speed of the motor vehicles.

Aronsson(2006) carried out a study to estimate the mean speed on different types of

streets. She applied linear regression model for the analysis. Different types of streets are

categorized according to the service they provide; Arterial, Suburban and Urban streets.

Various street characteristics such as traffic flow, on-street parking, crossing pedestrians,

and number of lanes are tested in a linear regression model to estimate their impacts on the

space mean speed. The result showed that on-street parking has negative impact on the

space mean speed by (-5.54). In a similar study, Nordström (2006) took into account mixed

traffic in the city centers and applied linear regression model to estimate the space mean

speed as dependant variable. According to the study, the presence of on-street parking was

found to reduce the average speed by 3km/h.



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Mean Speed-Urban Streets

Predictors Estimates

Intercept 39.8

Flow -0.202

Pedestrian -0.237

Lanes 5.24

bic_sep 4.73

Parking -5.54

Table 2.1: Mean speed model developed at KTH by Aronsson (2006), coefficients of the

regression analysis.



Intercept 45.4

Flow -0.171

bic_sep -6.31

Parking -2.93

Table 2.2: Mean speed model developed at KTH by Nordström (2006), coefficients of the

regression analysis.

A study about speed-flow relationship and roadside characteristic was also carried out by

Aronsson (2006). And it was reported that Wang et al (2006) built a model with which the

operating speed in urban streets is estimated. The author used different roadside

characteristics in the regression analysis to estimate the parameters from second-by-second

in-vehicle GPS data from two hundred randomly selected vehicles in Atlanta, Georgia, USA.

The result shows that on-street parking reduces the speed by 3.19 miles/h. See the Table 2.3

below for the estimates



15


Intercept 31.56

Number of lanes 6.46

Number of roadside objects/mile -0.1

Number of intersection/mile -0.08

Number of driveways/mile -0.05

Kerb indicator 3.01

Pavement indicator -4.26

Parking indicator -3.19

Land use 1 3.31

Land use 2 3.27

Table 2.3: Operating speed model developed by Wang (miles/hour).

With another approach Tivector(2009) studied the influence of on-street parking on the

average speed of urban street. On the study the streets were evaluated for one-sided and

two-sided on-street parking. The results of the study are consistent with the results of the

studies above showing a higher speed reduction for two-sided on-street parking. For two-

sided parking the speed reduction is 8.9km/h while for one-sided it is 4.5km/h. From the

results it is obvious that the impact is doubled on the two sided on-street parking when

compared to the one-sided on-street parking.

Table 2.4 below shows the speed model developed by Trivector; the effect of one-sided and

two sided on-street parking estimates.

Mean Speed-Urban Streets (50 km/h)


Intercept 38.7

Street type Local 0

Main 7.6

Parking One-sided -4.5

Two-sided -8.9

Table 2.4: Speed Model by Trivector (2009) (speed limit = 50 km/h)

Hansen (2007) conducted a similar study in US to find out the effect of the roadside

environment on the chosen speed. Data was collected from 272 road segments classified by

the urban roads and highways. Linear regression was used to analyze the data; analysis of



16

the variance was also employed to identify the significant variables. The variables used as

independent variables were pavement width, shoulder width, lane width, sidewalk width,

planting strips, building setbacks, access density and land use types. Table 2.5 shows the

results for the urban model; the on-street parking has been divided in to two levels of

utilization as heavy parking and light parking. The demarcation is 30% utilization. Whenever

the block is occupied by parked cars for the space greater than 30% called heavy parking,

otherwise light parking. They report that heavy parking has significant effect reducing the

chosen speed by 2.33mph. Table 2.5 below shows the predictors and estimates of the

model developed by Hansen (2007).



Intercept 29.94

Speed limit 35 mph 2.53

Speed limit 30 mph 2.09

Suburban residential 7.32

Urban residential 5.75

General urban 4.83

Parking >30% -2.33

Table 2.5: Speed Model by Hansen (2007) (miles/h)



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3 Methodology The data used for this project is a video recording which lasts for about eight and half hour

for each site. This data was collected in September 2009 by the division. From this raw data

intensive observation was made to collect all the necessary information.

3.1 Micro Study

3.1.1 Free Flow Speed

According to the literatures free flow speed is the term used to describe the mean speed of

a vehicles in a segment when there is no congestion or adverse effect,(e.g. bad weather).

For this study the free flow speed is the speed of vehicles without any obstruction of the

parking maneuvers, pedestrians, bicyclists and other events which could disturb the smooth

ride of vehicles.

For this thesis purpose two case studies are carried out. As per the requirements for free

flow speed condition vehicles were selected fairly from all the time periods. A minimum of

20 vehicles were selected for each case study. For the micro study the vehicles that are

under the ideal free flow condition were considered.

In the micro analysis a minimum of 20 vehicles were observed. The average travel time and

space mean speed was calculated. The procedures followed are:

1. The time to travel every 10 meter segments was registered

2. The speed at every 10 meter points was calculated

3. The average travel time at each 10 meter segment was calculated; the average of all

the observations at subsequent 10 meters is calculated for each category.

4. The average speed at each interval was calculated

5. Finally the (time-space) and speed profile graphs for each type of event were plotted

using the free flow condition as a base line.

3.1.2 Parking Maneuver

As it was already explained in the objective part, the main focus of this thesis is to see the

impact of road side events e.g. parking maneuver, double parking and load/unload

maneuver on the traffic performance of the urban street. As it is obvious from experience,

with the occurrence of parking maneuvers the vehicles behind will be held back from their

normal driving condition. Practically, it is not an easy task to isolate whether an impact has

come typically from a parking maneuver or some other events in a flock of vehicles which

are on a dynamic situation. It needs a careful observation for the whole segment. From the

raw data, the video recording which lasts for an eight and half hour duration for each site,

all the events were recorded. For the micro study out of the whole events recorded those

which are affected only by the event of interest are categorized under a specific group. Then



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the average impact of the vehicles was calculated. As the study is to emphasize the impact

of the parking maneuvers on the traffic performance of the street, the data was filtered in

order to exclude the condition in which the traffic is affected by other factors like the traffic

light when it is at the red.

On the streets there are many events which disturb the flowing traffic. These disruptions

make the drivers reduce their speed from their desired driving speed. Some of the

conditions besides the parking maneuver under which the vehicles might also be disturbed:

1. Queue due to signal

Some vehicles make a speed reduction due to the parking maneuvers. But after they

overtake the vehicle which is making the maneuver they will then be held once again

due to the red traffic light at the end of the block. In addition to this, in some cases the

events which involve parking maneuver were also accompanied by a queue which was

developed due to the traffic light. In these situations the drivers will be affected by multi

events, and it will not be an easy exercise to distribute the effects to their particularly

causing event. Thus, reduction in the traffic performance is not a pure impact of the

parking maneuver. For this reason the vehicles in this condition were excluded from the

data set.

2. Pedestrians’ interference

On the zebra cross the pedestrians have the right of way to cross the street. On the

contrary to this, some pedestrians cross the road at locations other than the zebra cross.

Specially half way the block pedestrians mostly prefer to cross directly. Especially when

the vehicles appear to be in a queue and there exists a reasonably higher head-way, it

appears to be inviting.

Similarly, in the course of this study few of the parking maneuvers have pedestrians

crossing the road simultaneously. This condition definitely affects the drivers’

perception. For this reason, the maneuvers under this situation are excluded.

3. Bicyclists

On all of the study sites selected there exists a bicycle lane. But in some cases the

bicyclists jump in to the motorized vehicles lane. This condition is approached in two

ways. When the parking lane is already occupied and vehicles will be forced to make a

double parking and they always block the bicycle lane totally. This will force the bicycles

to mix with the traffic and affect the vehicular movement. The root cause of this event is

double parking. Therefore, these events are considered under double parking. But

vehicles which are affected simply by the interference of bicycles without a double

parking event are excluded.



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4. Isolated parking events

This scenario includes the vehicles which are not disturbed by events other than parking

maneuvers, double parking or load/unload condition maneuvers. If these events were not

there the vehicles would have been on a normal driving condition. Those are the vehicles

which are considered in this study. Out of the whole parking maneuvers happening

throughout the day limited number of vehicles were found in this situation.

For the micro study, using the free flow line as a base, the deviation is measured for each

category of events. These graphs are presented in Chapter 5, results and discussions. They

demonstrate the delay of the vehicles. More discussion could be found on the result part.

Sometimes, maneuvers happen while there is a long distance gap (distance head-way) so

that the maneuvers will not have a significant effect on the smooth flow of the traffic. From

the observation on the video it can easily be observed that drivers take enough time to have

good distance headway when they are making out-bound maneuvers. This happens because

they will have enough time to think about the proper gap acceptance which is not the case

for inbound maneuvers. The presence of bicycle lane also helps for this phenomenon. It

helps as a buffer area for the out bounding vehicles till they get a reasonable distance-head

way. So the out bounding maneuvers appear to have a bit lesser effect on the traffic

stream. In some cases the maneuvers with reasonably higher distance-headway are

excluded as they have negligible impact on the following car.

For the vehicles under Isolated parking maneuver condition the following procedures were

carried out

1. The time to travel every 10 meter segments was registered

2. The speed at every 10 meter points was calculated

3. The average travel time at each 10 meter segment was calculated; the average of all

the observations at subsequent 10 meters is calculated for each category.

4. The average speed at each interval was calculated

5. Finally the (time-space) and speed profile graphs for each type of event were plotted

using the free flow condition as a base line.

3.2 The Macro Study

In this approach all the vehicles are included in the data analysis. In the events where there

happens to be no parking maneuver, double parking or load/unload maneuver, the travel

time was measured excluding the effect of other speed reducing factors like crossing

pedestrians, bicyclists and other unusual events like the over speeding of emergency



20

vehicles and police car chase. Sometimes due to the fact that the area is CBD (Central

business district) some drivers happen to be biased to choose their free flow speed. Those

vehicles were excluded from the analysis due to the fact that they will affect the final result.

And for the macro study the travel time was averaged for the vehicles which are not

affected by any event and the traffic light.

For the macro analysis, the ultimate target is to analyze the situation in every five minute

intervals and use each five minute interval as a single data for the regression analysis.

3.2.1 Data Analysis/Processing

For the Macro study all the maneuvers will be considered. In this case all the maneuvers will

be aggregated to five minute time intervals. The observed time segment (eight and half) is

sub-divided in to a five minute interval. Each site will have about 102 intervals. But

sometimes there are intervals with a less suitable condition to be considered as a data.

These types of data were excluded.

The following general procedures were implemented on the data reduction:

- All the vehicles passing through the street segment were counted. With this the flow

for each of the five minute intervals was calculated.

- The average travel time in each five minute time interval was calculated.

- The average speed was calculated using the length of the street segment.

- The maneuvers were summed up under each category; the inbound, reverse

inbound and outbound maneuvers are taken under the category of maneuver, the

presence of double parking and Load/Unload activity was checked under each time

interval.

- In order to analyze the data, regression was applied. In the regression analysis the

impact of different variables on speed was analyzed. In this model building the

dependent and the independent variables need to be selected. Generally a table

containing speed, flow, maneuvers, double parking, the load/unload, lane width, and

number of lanes was developed.

For this analysis the space mean speed was used. In order to calculate the space mean

speed the travel time was measured for all the vehicles which are to be considered for the

study. The travel time was measured using Semi Automated Vehicle Analysis (SAVA),

software developed at KTH by Jeff Archer (2006).

In a similar manner to the micro study the vehicles which are under the impact of the red

traffic light were excluded. In every five minute interval the vehicles were counted in order

to know the flow. Basically, the vehicles under the green light were considered.



21

3.2.2 Regression Analysis

3.2.2.1 Steps followed in regression analysis

According to Chatterjee and S.Hadi(2006) in their book ‘Regression Analysis by example’

here are some steps which need to be included in a regression analysis. Most of those steps

are included in the regression analysis of this thesis work and these include:

� Statement of the problem

� Selection of the relevant variables

� Data collection

� Model specification

� Choice of fitting method

� Model fitting

� Model validation and criticism

� Using the chosen model for the solution of the posed problem

The steps mentioned above are explained as follows,

1. Statement of the problem

Usually problem formulation is the first step in regression analysis. Under this step

formulation of the question(s) to be addressed by the analysis is included. Statement of

the problem is the first and the most decisive part in regression analysis. Its significance

comes due to the fact that wrongly defined problem will result in wastage of effort. In

this thesis the main problem which is intended to be addressed is the impact of parking

maneuvers. Normally there are various ways in which performance will be explained.

Typically on this study the speed reduction due to the parking maneuver and related

events will be analyzed. This means, the loss on the potential capacity of the urban

roads due to some activities happening on the road side will be studied. This is a

quantitative analysis on which the travel time is measured and the speed will be

calculated as the distance is known. Travel time of vehicles vary due to various factors

but for this study the situations in which travel time is affected by the factors chosen as

an independent variable are considered.

2. Selection of the relevant variables

Selection of the relevant variables is the next step after defining statement of the

problem. In this study the main aim is to see the impact of parking maneuvers on the

traffic performance, double parked vehicles and the load/unload vehicles. For this

purpose different category of events was developed. This includes the maneuvers,

double parking and load/unload maneuvers. The maneuver embraces front inbound,



22

reverse inbound and outbound maneuvers. In addition to these flow, the lane width and

number of lanes are considered as independent variables. Besides these the bicyclists

and pedestrians were included as relevant parameters. But on the preliminary analysis

the two variables happen to be insignificant. Therefore, they are excluded. The

dependent variable is the space mean speed and the independent variables are flow,

maneuver, double parking, load/unload, lane width and number of lanes.

3. Data Collection

After selecting the potentially relevant variables the next step is data collection.

Sometimes data collection needs to be done in a controlled setting in the environment

under the study to be used in the analysis. This is done to hold the effect of those factors

which are not of primary interest. Usually, the data collection is carried out under non-

experimental conditions where very little is controlled by the researcher. In this study

there is a video recording of 4hours and 30minutes in the morning and a 4hour

recording in the afternoon. From this raw data the number of maneuvers under each

category was counted. Besides this with the help of SAVA the travel time of all the

vehicles which are not disturbed by the traffic light was measured. The number of

vehicles using the street in each five minute segment was counted; with this the flow

will be determined.

4. Model Specification

The model that is suitable to analyze cause and effect is regression analysis. In this thesis

the attempt is to evaluate the effect that comes on the speed due to numbers of

variables. Therefore, multiple regression equation will be developed to deal with the

situation. According to Chatterjee and S. Hadi on their book titled Regression by

example, multiple regression equation is an equation containing more than one

predictor variable.

5. Model Parameter Estimation

The next step after the Model specification is model parameter estimation. There are

various estimation methods. The most commons are ordinary least square method,

maximum likelihood, the ridge method and others. Lots of statistical packages do the

analysis. For this study Microsoft Excel will be used to make the model fitting, to

estimate the regression parameters or to fit the model to the collected data. The

estimates of the regression parameters (βo, β1, β2,….) are called the predictors. They

are used to analyze effects of policy that involves changing values of the predictor

variables or to forecast values of the dependent variable for a given predictor set up.

With the help of regression analysis it is possible to understand the interrelationships

among variables in a specific environment or circumstance. Sometimes the effect that



23

comes due to some covered values might be more valuable than the predictor variables

on the equation.

Model validation and criticism is done in the result part of the regression analysis.

3.2.2.2 Variable Definition

The regression analysis in this thesis work includes the following variables:

� Space mean speed (dependent variable)

� Flow (Independent variable)

� Maneuvers (Independent variable)

� Double parking (Independent variable)

� Load/unload maneuver (Independent variable)

� Number of lanes (Independent variable)

1. Space Mean Speed

The effect of different variables on traffic performance of an urban street is studied. In

order to see the traffic performance space mean speed is selected as a representative

parameter. In this study the impact of some events on the space mean speed was

analyzed. For this reason space mean speed was selected as a dependent variable. The

travel time which takes to cross a known distance segment was measured. Normally for

this study the vehicles were selected in a controlled way. Vehicles could be disturbed

from their normal driving condition for different reasons. Most of the factors which

could make this delay are considered as independent variables. As it is explained on the

description of the case studies, there is a traffic light at the end of the block. This

condition brought a major challenge to select the proper vehicles for the study. In this

study the delay due to the traffic light is not a major concern. Therefore one of the

major factors to select vehicles is green traffic signal light. But besides this a long queue

sometimes gets developed and a single green cycle will not be enough to dissipate the

queue. Considering the entire mentioned criterion, the travel time was measured for all

the vehicles. Then, an aggregate of five minutes was made for the whole day. All the

measurements in five minutes were averaged to a single value. In this analysis there are

481 different observations. Each of these observations represents events in a five

minute interval. Some vehicles enter the section in one five minute interval and leave

the block in another five minute interval. Those observations are excluded from the data

set to avoid consequent error.



24

2. Flow

The flow in an urban road is a dynamic factor which changes in every period of time. In

this study one of the factors which are expected to affect the speed of a vehicle is flow.

As it is an urban street segment, flow varies with time of the day. As it can visually be

observed from the video of the site most of the queuing periods happen when the flow

is high. For this study all the vehicles using the street segment are counted for every five

minute interval. And finally the 481 observations corresponding to the averaged space

mean speed are considered.

3. Parking Maneuvers

The other most important variable which is considered to affect the speed of the

vehicles is parking maneuver. It is the activity of vehicles to leave the drive way to the

parking lane or join the moving traffic from the parking lane. Parking maneuver

embraces various types of events; the forward inbound maneuver, reverse inbound

maneuver, and outbound maneuver. These maneuvers have different impacts on the

travel time or speed of moving vehicles, according to the visual inspection the inbound

maneuvers happen to increase the travel time a bit more than the out bound. The

reverse inbound maneuvers happen to take more time than the forward inbound

maneuvers. For this reason their impact will also be relatively higher. Normally the

presence of the bicycle lane on the street gives a good platoon for out bounding

vehicles. The vehicles leave their position and wait for an acceptable gap on the traffic

stream being at an angular position. Similarly, for inbound maneuvers specially to make

a reverse maneuver some vehicles take aside and double park on the bicycle lane wait

for an acceptable gap. For this study all the mentioned events were counted for every

five minutes corresponding to the measured space mean speed. In some of the five

minute intervals without any of such events a null value was assigned.

4. Double Parking

Sometimes vehicles would like to stop at a specific location which is already occupied.

This happens when delivery vans or trucks have to park in front of a specific store or

sometimes when private cars have to drop passengers. Usually double parking takes

place in the conditions that the driver is not going to leave the vehicle or if he is going to

be in around. Taxis, delivery vans and delivery Lorries are the most common types of

vehicle to make such a parking. Usually this type of parking blocks the bicycle lane so

that the bicycles will be forced to mix with the flowing vehicles. In most of the cases the

vehicles give way to the bicycles by reducing their speed. In such situation the vehicles

will have a higher travel time. Besides this double parked truck or bigger size vans take

some space from the traffic lane in addition to the bicycle lane. This will force the drivers

to have a lower speed so that the travel time will be increased. For this study the

presence of double parked vehicles will be checked and assigned 1 in every five minute



25

observation. Some of the vehicles in the study stay in the double parking condition for

more than five minutes; hence they happen to be in two time periods or more. And for

the time periods without a double parking, a null value was assigned. In other words

double parking is analyzed as a dummy variable.

5. Load and Unload Maneuvers

These are maneuver of the vehicles which deliver goods for some stores and groceries.

Normally, there is a special parking space for these vehicles. It is called Last Zone and is a

region restricted only to the delivery trucks and vans. Usually it is a common practice to

put a sign post for the time restriction and destination of the Last Zone. In addition to

this the curb will be painted yellow. Parking other vehicles in this zone is forbidden and a

passenger vehicle doing so is liable to a fine. Despite this fact some passenger vehicles

park at these spots. This forces the delivery vehicles to make a double parking. This will

affect the traffic significantly. In these special cases the load/unload maneuver will be

taken as double parking instead. This event is not significantly different from the normal

parking maneuver. The specialty with load/unload maneuver is that; it takes place at a

specified location and it is an exercise of delivery vans and light trucks only. This

independent variable is taken as a dummy variable.

6. Number of lanes

The data set consists of one lane and two lane streets. Therefore, to capture the impact

that comes due to the increase in the number of lanes, this variable is introduced.

7. Shock wave effect

This variable is introduced to capture the effect of events in successive five minute time

interval. It is introduced as a dummy variable. This means that if any type of events happen

in the previous five minute interval it will be assigned 1 and 0 otherwise. All of the stretches

taken for this study are not longer than 120 meter. So the impact of the events will be

propagated to the adjacent blocks and subsequently to the next five minute interval. This

situation will be captured by the shock wave effect variable.

3.2.2.3 Expected Signs and Initial Hypothesis

From the entire description above, there follow some initial hypotheses which could be

tested with the analysis of the model. These hypotheses are:

1. With the increase of flow the space mean speed is expected to reduce. For this

reason the coefficient of flow is expected to be negative.

2. In the same manner when parking maneuvers happen the following vehicle always

gets slowed down. From this it will be obvious that the parking maneuver is

expected to have a negative coefficient.



26

3. As it is explained above usually the double parking events narrow the lane width and

block the bicycle lane. For this and other reasons drivers will be forced to chose a

much lower speed to drive safely in such streets. So double parking is also expected

to have a negative coefficient.

4. Normally the load/unload maneuvers are the same as the other maneuvers except

that they take place at a specified stretch of the street called Last Zone. Therefore,

this variable is also expected to have a negative coefficient.

5. In multi-lane streets when various events happen the following vehicle will have

enough space to escape from the disruption. For this and other reasons drivers will

have a bit higher speed in multi-lane streets. As the number of lanes increase drivers

choose higher speeds. Therefore, the number of lanes variable is expected to have a

positive coefficient.

6. As it was explained in the variable definition, the light truck and vans which double

park at any location along the street for the purpose of delivering goods to stores

and supermarkets fall under the category of double parking. These vehicles are

normally expected to have a higher reductive effect. The double parking variable is

expected to have a higher magnitude than the other variables.

7. Because the dependent variable is space mean speed the intercept is expected to

have a positive coefficient.

3.2.2.4 Semi Automated Vehicle Analysis (SAVA) Software

SAVA software is a video analysis tool that is used for this study. On SAVA it is possible to

measure the travel time and distance. The output from SAVA was used both in the micro

analysis and macro analysis.

Procedures in SAVA

Micro Analysis

- Always the first step when dealing with SAVA is configuring the coordinates. To do

these a known rectangular area is taken in the video. The dimensions and the

diagonal of the rectangle are calibrated. Doing so the software will understand all

the distances in the video. After the calibration it will be possible to measure

distance at any location in the video.

- After calibrating the coordinates virtual lines are drawn with 10 meter interval.

- The time in the software is synchronized with the real time in the video recording.

- The time stamp at every 10 meter interval will be recorded to a text file output by

the software.

Macro analysis

- The coordinates are configured in a similar manner as in the micro study.



27

- The time is also synchronized in the same way.

- Here the virtual lines are drawn at the entry and exit points. It is drawn at two

locations (at the start and end of the specified segment which is the interest of

study).

- The time stamp at the location was recorded for the vehicles of interest.

4 Description of Case Study (Data Analysis) Basically three case studies are carried out. As it was already mentioned in the

methodology part in Chapter 3, two approaches are used in this study: the Micro

analysis and the Macro analysis. In the former one, three case studies are carried out.

For the Macro study two different spots are used from each case study except the last

one. For all of the spots an eight and a half hour study period is selected.

4.1 Case Study One

In this case study, two street segments are studied; they are named as Fleminggatan site

one and Fleminggatan site two.

Fleminggatan site one is the street segment which is found between Inedalsgatan and

Polhemsgatan. This site has different activities and it incorporates special zones which are

dedicated to the parking activity of specific vehicles. These include the last zone which is

dedicated to only delivery vehicles and a restricted parking area for handicap cars. There is a

hospital in this area and there are also many stores which increase the road side activity.

The general road geometry is that it is a two lanes street which is situated between

signalized intersections. The lane width is 3.3 meters and it has a bicycle lane which is 1.5

meter wide and located between the parking lane and traffic. The parking lane is 2.6 meters

wide. On this street a 120 meter stretch was considered for the study. This site is one of the

streets which lead towards the city center and it is a relatively busy street with a higher

flow. On the road side of this street segment around 20 vehicles could be parked excluding

the Last Zone.



28

Figure 4.1 Karta of Fleminggatan site one Source: Google map



29

Figure 4.2: Road geometry of Fleminggatan site one



30

Fleminggatan site two is the segment which is found between Wargentinsgatan and Norra

Agenegatan. This site also has different activities. There is a last zone which is a reserved

area only for delivery trucks and vans.

The general road geometry is not somehow different from site one except that there exist

some differences; it is a two lane street which is situated between signalized intersections.

The lane width is 3.8 meters and it has a bicycle lane which is 1.5 meter wide and located

between the park and the traffic. The parking lane is 2.6 meters wide. On this street a 140

meter stretch was considered for the study. This site is one of the streets which are coming

from the city center and it is a relatively busy street with a higher flow. On the road side of

this street segment around 22 vehicles could be parked excluding the Last Zone.

Figure 4.3: Karta of Fleminggatan site two Source: Google map



31

Figure 4.4: Road geometry of Fleminggatan site two



32

4.2 Case study Two

In this case study, one street segment with a variable lane width is studied. A street segment

in Hornsgatan was studied. This segment is situated between Torkel Knutssonsgatan and

Timmermansgatan. The specialty of this segment is that it is one lane for the first half of the

stretch and the width increases significantly for the next half so that is will be considered as

a two lane street.

In this case study the first and second half of the street are dealt separately for the Macro

analysis. The first half which is the one lane part incorporates a last zone in which

load/unload activity will be carried out. This area is an active business area so that lots of

parking maneuvers happen. Due to the high demand of the parking space numerous double

parking events are visualized on the video.

The general road geometry is that the first half of the street is one lane street. And it is

situated past a traffic signal. The lane width is 3.0 meters and it has a bicycle lane which is

1.5 meter wide. The parking lane is 2.6 meters wide. A 55 meter segment was taken for the

one lane part of the study. The other half of the segment has an average width of 5.2

meters so that it can be considered as a two lane. Normally a street width more than 5

meters is considered as a two lane street. It has a bicycle lane and parking lane which are

continuations of the first half. This street has a moderate flow than the previous case study.

Figure 4.5: Karta of Hornsgatan Source: Google map



33

Figure 4.6: Road geometry of Hornsgatan



34

4.3 Case study Three

In this case study, a street segment which is found between Borgmästargatan and Ersta

Trappor is studied. This site has different activities and it incorporates special zones which

are dedicated to the parking activity of specific vehicles. These include the last zones which

are dedicated to only delivery vehicles where the load/unload activities take place.

The general road geometry is that it is a one lane street which is situated between signalized

intersection and a pedestrian priority intersection. The total carriage way width is 5.9

meters. Of this width 2.6 meter is dedicated to the parking vehicles. Therefore, the lane

width will be 3.3 meters and it has a bicycle lane which is 1.5 meter wide. Unlike all the

other case studies the bicycle lane is located between the parking lane and the pedestrian

side walk. For all the other case studies the bicycle lane is located between the traffic

stream and the parking lane. On this street a 110 meter stretch was considered for the

study. This site is one of the streets which lead towards the city center and it is a relatively

busy street with a higher flow.

Figure 4.7: Karta of Folkungagatan Source: Google map



35

Figure 4.8: Road geometry of Folkungagatan



36

5 Results and Discussion 5.1 Micro Analysis

In the Micro analysis the data reduction is not done in a time bound manner. Some of the

interesting events of the same kind are categorized together to see their impact. The

interference of some other disturbing factors than the events of interest was more

challenging to exclude.

5.1.1 Case Study One

Micro analysis for case study one was carried out in fleminggatan site one. This site was the most

challenging to get the data for this study. This street is highly busy and lots of maneuvers happen

though. The problem is the street is occupied by queuing vehicles for most of the time. It was

difficult to find vehicles which are affected only by a specified event of interest. Due to the fact that

the street is so narrow and also there exists a long queue that generates from the red traffic signal

simultaneous events happen most of the time. For this reason lots of vehicles were excluded from

the data set. The results listed for this case study are somewhat indicative but should not be

conclusive.

5.1.1.1 Free Flow Speed Trajectory

Figure 5.1: Average travel time trajectory for every 10 meter segment

0

2

4

6

8

10

12

0 20 40 60 80 100 120 140

Av

era

ge

tra

ve

l ti

me

(se

con

ds)

Space(meters)

Average Travel time Trajectory

Travel time



37

Figure 5.2: The space mean speed at every 10 meter space

The average travel time and space mean speed are tabulated below

space Average

travel time

speed

10 0.98352941 35.32548

20 2.04117647 36.28527

30 3.09411765 36.69526

40 3.94470588 42.05376

50 4.73882353 43.8492

60 5.65176471 41.85049

70 6.52705882 41.01211

80 7.42941176 40.8702

90 8.29882353 41.3971

100 9.17647059 40.99098

110 10.1082353 38.73615

120 11.1023529 38.07602

Table 5.1: Average travel time and space mean speed

The results above give the average of 33 travel time and space speed of vehicles which are

on a free flow condition. The graph for travel time appears to be perfectly linear. This tells

that the time that takes the vehicles to travel every 10 meter segment is nearly equal. From

Figure 5.1 above it can be seen that the space mean speed is around 40 km/h; it gets

progressively increasing to some distance and falls again. This is because of the fact that the

0

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Space(meter)

Space MeanSpeed

speed



38

drivers come from a lower speed at the entry to the block and gets decelerated at the exit

of the street. In the graph it can also be seen that the vehicles attain the maximum driving

speed around half way the block and start decelerating. The average of the speeds at the 12

location is 39.76km/hr. So this can be taken as the free flow speed for this street.

5.1.1.2 Impact of Inbound Maneuvers


The inbound maneuvers happen to shift the graph upper than the free flow condition. The

travel times are measured for 10 meter segments. The difference between the cumulated

travel times, which is the average travel time at the 120 meter space, will give us the delay

which comes due to the event; inbound maneuver in this case. As it was already explained

in the methodology in Chapter 3, in the micro analysis the vehicles which are affected only

with the inbound maneuver are considered. So these vehicles are typically affected by the

event of interest. From the above graph, the private cars have an average delay of around

3.2 seconds.

0

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0 20 40 60 80 100 120 140

Av

ere

ag

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rav

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Space(meter)

Impact of Inbound Manuevers on Travel Time

Free flowing cars

Private cars



39


In Figure 5.4 above the private cars have an average speed lower than the free flow speed.

As presented earlier, the average free flow speed is 39.76 km/hr. And the average of the

space speed at the 12 locations happens to be 32.38km/hr for private cars when they are

affected by the inbound parking maneuver. The space speed plot for the inbounding event

affected vehicles happens to be in a harmonic way with the free flowing vehicles. As a result

the gap between the graph lines is mostly constant.

0

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0 20 40 60 80 100 120 140

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Space(meter)

Impact of Inbound Manuevers on Speed

Free flowing cars

Private cars



40

5.1.1.3 Impact of Outbound Maneuvers


As it can be seen from Figure 5.5 above, the outbound maneuvers happen to affect the

traffic in a same way as the inbound maneuver. As it was mentioned in the methodology

part in Chapter 3, out bounding vehicles take enough time to have a good gap acceptance.

This results in a lesser impact on the normal traffic. The average delay is around 4 second

for the private cars.

0

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0 20 40 60 80 100 120 140

Av

era

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Space(meter)

Impact of Outbound Manuevers on Travel Time

Free flowing cars

Private cars



41


As it can be seen from Figure 5.6 above the private cars have an average speed lesser than

the free flow speed. The average space speed is 32km/hr for private cars. This implies a

reasonable speed drop due to outbound maneuvers. As it can be seen from the plot the

lowest point is around 30 meters from the start of the block this implies there exist a

relatively higher amount of out bounding maneuvers at the specified location.

0

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Space(meter)

Impact of Outbound Manuevers on Speed

Free flowing cars

Private cars



42

5.1.1.4 Impact of Double Parked Vehicles


As it is shown in Figure 5.7 above, the travel time happens to be increased due to the

double parked vehicles. Thus, the vehicles will be delayed by 2seconds due to the double

parked vehicles. Normally, most of the double parking narrows the lane width. This will

result in a higher travel time of the vehicles because of the fact that they choose a safe

speed which is definitely lower than the normal free flowing speed.

0

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14

0 20 40 60 80 100 120 140

Av

era

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space(meter)

Impact of Double Parking on Travel Time

Free flowing cars

Private cars



43


The space mean speed of the vehicles happens to be lower than the space mean speed of

the free flowing vehicles. The space mean speed for private cars is 35.74km/hr. The

difference happens to be a bit lower when it is compared with that of the inbound and

outbound maneuvers. When there is a double parked on the street the drivers choose lower

speed than the free flow speed because the lane will be narrowed. Besides this the bicycle

lane will also be blocked so that the bicycles also get mixed with traffic and the vehicles will

be affected.

0

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Space(meter)

Impact of Double Parking on Speed

Free flowing cars

Private cars



44

5.1.1.5 Impact of Load/Unload


As it can be seen from the graph above the load/unload maneuvers shift the travel time

graph up. This is because of the fact that these maneuvering activities increase the travel

time. The load/unload maneuvers increase the average travel time by 4seconds. Despite the

fact that these maneuvers are similar to the other inbound and out bounding maneuvers,

they have a bit higher impact. This comes due to some reasons: the first reason is the

vehicles which are making the maneuver under this category are light trucks and Vans. And

the other reason is the load/unload happens in the last zone only. So the drivers have to

make sure that they are parking at the specified location. In doing so, they affect the

following vehicles more than the other maneuvers.

0

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Av

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Space(meter)

Impact of Load/Unload on Travel Time

Free flowing cars

Private cars



45


As is it seen in Figure 5.10 above, the space mean speed gets reduced with the load/unload

vehicles. The space mean speed with the presence of load/unload vehicles is 31.7km/hr.

This gives a significant speed reduction due to the load/unload vehicles. One significant

thing that can also be seen from this figure is that, at 60 meter distance the speed drops

significantly. On the road geometry, this location is where the last zone occurs. As it was

already mentioned the specialty of this maneuver is the entire maneuvers take place at a

specific location. Therefore the averages of all the impacts also manifest this fact.

0

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Space(meter)

Impact of Load/Unload on Speed

Free flowing cars

Private cars



46

5.1.2 Case Study Two

The data for the Micro and Macro analysis of case study two is collected from Hornsgatan. This

street is also busy. As it was already mentioned on the description part this street is single lane with

a lane width of 3 meters and gets wider to 5.2 meters after half way and could be taken as a two

lane street per direction. This street is relatively good site for the study. The interference of red

traffic signal is relatively less. On top of this the number of simultaneous events is less so that it is

easy to take a specific event of interest without the interference of the other events. The draw back

with this site is that there is no last zone in the direction of study. In this site it was easy and enough

data was found to study the reverse inbounding maneuvers.

5.1.2.1 Free Flow Speed


0

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0 20 40 60 80 100 120 140

Av

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Space(meter)

Avereage travel time




47

Figure 5.12: The space mean speed trajectory at every 10 meter space

Space Average

travel

time

Space mean

speed

0 0

10 0.956 39.11627113

20 1.852 41.38735078

30 2.716 42.48042168

40 3.518 45.99270783

50 4.32 44.60373067

60 5.142 45.39387671

70 5.964 44.42729233

80 6.764 44.7701295

90 7.648 41.35871816

100 8.574 39.97237216

110 9.56 37.63398698

120 10.544 37.41055392


The result above gives the average cumulated travel time and space mean speed of 25

vehicles. The graph of travel time appears to be perfectly linear. This implies that the time

that every vehicle takes to cover all the 10 meter segments is nearly equal. From figure 5.2 it

can be seen that for most of the time the mean speed stays around 40 km/hr. But it a

similar pattern to case study one the vehicles start accelerating up to half way the block and

start to decelerate. The average of the 12 locations is 42km/hr.

0

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0 20 40 60 80 100 120 140

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Space(meter)

Space mean Speed

Space mean Speed



48



The inbound maneuvers happen to shift the graph to the upper left from the free flow

condition. The travel times were measured in the same way as case study one for every 10

meter intervals. The vertical difference at the 120 meter location is taken as delay due to

the event. As per this consideration the delay due to the inbound maneuver is calculated to

be 3.2seconds.

0

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0 50 100 150

Av

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space (meter)

Impact of Inbound manueveres on travel time

Free flow

Inbound manuever affected



49


As it can be seen form Figure 5.14 above the graph for the vehicles affected by inbound

maneuvers lies below the graph for the free flow condition. One interesting fact that can be

seen from the graph is that in both cases the pattern is nearly similar. The gap between both

the graphs is nearly equal. The average of the space speed at the 12 locations is 34.07

km/hr. As it was explained above the average free flow speed is 42km/hr.

5.1.2.3 Impact of Reverse Inbound Maneuvers

Normally the reverse inbound maneuvers take more time than the front inbound

maneuvers. As it can be seen in the video the vehicles take aside and double park in the

bicycle lane till they find enough headway to make the maneuver. They have a bit higher

impact than the front inbounding maneuvers. The delay due to this event is about 4seconds.

In a similar way the space mean speed is also less than the case for front inbound

maneuvers. The space mean speed for this case is 32.80km/hr.

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

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50,00

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d(

Km

/hr)

Space (meter)

Impact of Inbound Manuevers on speed

free flow speed

Inbound manuever affected



50

Figure 5.15: Impact of reverse inbound maneuvers on average travel time

Figure 5.16: impact of reverse inbound maneuvers in space speed

0

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Impact of Reverse Inbound Manuevers in travel

time

Free flowing vehicles

Reverse inbound

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

50,00

0 20 40 60 80 100 120 140

Av

era

ge

tra

ve

l ti

me

(S

eco

nd

s)

Space (meter)

Impact of Reverse inbound Manuevers in travel

time

Free flow Speed

Reverse inbound manuever

affected



51


Figure 5.17: Impact of outbound parking maneuvers on travel time

In a similar way to case study one the outbound maneuvers have lesser impact on the

average travel time than the inbound parking maneuvers. As it can be seen from the graph

the outbound maneuvers shift the graph from the free flow condition a bit. The delay is

around 3.5seconds.

Figure 5.18: Impact of outbound parking maneuvers in space speed

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140Av

era

ge

tra

ve

l ti

me

(S

eco

nd

s)

Space (meter)

Impact of outbound manuevers in travel

time


Outbound manuever

affected vehicles

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

50,00

0 50 100 150

Sp

ace

me

an

sp

ee

d(K

m/h

r)

Space (meter)

Impact of Outbound Manuevers

Free fowing vehicles

Outbound manuever affected

vehicles



52

As it can be seen Figure 5.18 above the average space speed of the vehicles affected by the

outbound parking maneuvers happens to lie below the free flow condition. And they are

more or less following the same pattern. The average of the 12 observation is 32.87km/hr.

This speed reduction is less than the one caused by the inbounding parking maneuvers.

5.1.2.5 Impact of Double Parking

Figure 5.19: Impact of double parking on average travel time

As it can be seen from figure 5.19 above, the curve is shifted to the upper left. The double

parking events increase the average travel time of the street. The average delay due to

these events is around 4seconds.

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140

Av

era

ge

tra

ve

l ti

me

(se

con

ds)

Space (meter)

Impact of double parking on travel time


Double parking affected

vehicles



53

Figure 5.20: Impact of double parking on space mean speed

As can be seen from figure 5.20, the space mean speed line for vehicles is affected by

double parking lies below the free flow line. Throughout the stretch the shift is almost

constant. And like the other mentioned events, the line follow almost similar pattern.

Almost around half way the block the vehicles attain the peak speed. The average of the 12

points is 31.6km/hr. In a similar manner to the case study one the double parking happens

to have significant effect than all the other events.

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

50,00

0 20 40 60 80 100 120 140

Sp

ace

me

an

sp

ee

d (

Km

/hr)

space (meter)

Impact of Double parking on space mean speed

Free flowing Vehicles

Double parking affected

vehicles



54

5.1.3 Case Study Three

The data for the Micro and Macro analysis of case study three is collected from Folkungagatan. This

street is also busy. This street is also a way for number of bus lines including the blue line. As it was

already mentioned on the description part this street is single lane with a lane width of 3.3meters.

This street is also good site for the study. The interference of red traffic signal is relatively less. The

number of simultaneous events is less so that it is easy to take a specific event of interest without

the interference of the other events. The specialty of this site is the bicycle lane is located between

the outermost lane on which the vehicles make on-street parking and the pedestrians walk way. For

this reason the drivers barley make double parking on this site. Therefore, enough data was not

found for the analysis of the impact of double parking on this site.

5.1.3.1 Free Flow Speed


0

2

4

6

8

10

12

0 20 40 60 80 100 120

Av

ere

ag

e t

rav

el

tim

e(s

eco

nd

s)

Space(meter)

Avereage Travel time




55


space Average

Travel

time

Speed

10 0.956 36.61414

20 1.852 39.22082

30 2.716 40.25884

40 3.518 40.22515

50 4.32 41.53121

60 5.142 41.44147

70 5.964 42.57149

80 6.764 42.93759

90 7.648 43.99609

100 8.574 43.95564

110 9.56 39.8731


The results above give the average of travel time and space mean speed of 27 vehicles

which are on a free flow condition. The graph for travel time appears to be perfectly linear.

This tells that the time that takes the vehicles to travel every 10 meter segment is nearly

equal. From Figure 5.22 above it can be seen that the space mean speed is around 41 km/h;

0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120

Sp

ace

me

an

Sp

ee

d(K

m/h

r)

Space(meter)

Space mean Speed

Space mean speed



56

it gets progressively increasing to some distance and falls again. This is because of the fact

that the drivers come from a lower speed at the entry to the block and gets decelerated at

the exit of the street. In the graph it can also be seen that the vehicles attain the maximum

driving speed around half way the block and start decelerating.



The inbound maneuvers happen to shift the graph to the upper left from the free flow

condition. The travel times were measured in the same way as case study one and case two

for every 10 meter intervals. The vertical difference at the 110 meter location is taken as

delay due to the event. As per this consideration the delay due to the inbound maneuver is

calculated to be 3seconds.

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120

Av

ere

ag

e t

rav

el

tim

e(s

eco

nd

s)

Space(meter)

Impact of Inbound Manuevers on Travel Time

Free flowing cars

Private cars



57


As it can be seen form Figure 5.24 above the graph for the vehicles affected by inbound

maneuvers lies below the graph for the free flow condition. One interesting fact that can be

seen from the graph is that in both cases the pattern is nearly similar. The gap between both

the graphs is nearly equal. The average of the space speed at the 11 locations is 33.06

km/hr. As it was explained above the average free flow speed is 41km/hr.

5.1.3.3 Impact of Reverse Inbound Maneuvers

Normally the reverse inbound maneuvers take more time than the front inbound

maneuvers. As it can be seen in the video the vehicles take aside and double park in the

bicycle lane till they find enough headway to make the maneuver. They have a bit higher

impact than the front inbounding maneuvers. The delay due to this event is about

3.5seconds. In a similar way the space mean speed is also less than the case for front

inbound maneuvers. The space mean speed for this case is 33.4km/hr.

0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120

Sp

ace

me

an

Sp

ee

d(K

m/h

r)

Space(meter)


Free flowing cars

Private cars



58

Figure 5.25: Impact of reverse inbound maneuvers on average travel time

Figure 5.26: impact of reverse inbound maneuvers in space speed

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120

Av

ere

ag

e t

rav

el

tim

e(s

eco

nd

s)

Space(meter)

Impact of Reverse Inbound Manuevers in

travel time

Free flowing cars

Private cars

0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120

Sp

ace

me

an

Sp

ee

d(K

m/h

r)

Space(meter)


Free flowing cars

Private cars



59


Figure 5.27: Impact of outbound parking maneuvers on travel time

In a similar way to case study one the outbound maneuvers have lesser impact on the

average travel time than the inbound parking maneuvers. As it can be seen from the graph

the outbound maneuvers shift the graph from the free flow condition a bit. The delay is

around 3seconds.

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120

Av

era

ge

tra

ve

lti

me

(se

con

ds)

Space(meter)

Impact of Outbound Manuevers on Travel

Time

Free flowing cars

Private cars



60

Figure 5.28: Impact of outbound parking maneuvers in space speed

As it can be seen Figure 5.28 above the average space speed of the vehicles affected by the

outbound parking maneuvers happens to lie below the free flow condition. And they are

more or less following the same pattern. The average of the 11 observation is 33.80km/hr.

This speed reduction is less than the one caused by the inbounding parking maneuvers.

5.1.3.5 Impact of Load/Unload


0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120

Sp

ace

me

an

sp

ee

d(K

m/h

r)

Space(meter)

Impact of Outbound Manuevers on Speed

Free flowing cars

Private cars

0

2

4

6

8

10

12

14

16

18

0 20 40 60 80 100 120

Av

ere

ag

e T

rav

el

Tim

e(S

eco

nd

s)

Space(meter)

Impact of Load/Unload on Travel Time

Free flowing cars

Private cars



61

As it can be seen from the graph above the load/unload maneuvers shift the travel time

graph up. This is because of the fact that these maneuvering activities increase the travel

time. The load/unload maneuvers increase the average travel time by 6.5seconds. Despite

the fact that these maneuvers are similar to the other inbound and out bounding

maneuvers, they have a higher impact in this case study. This comes due to some reasons:

the first reason is the vehicles which are making the maneuver under this category are

heavy trucks and Vans. And the other reason is the load/unload happens in the last zone

only. So the drivers have to make sure that they are parking at the specified location. In

doing so, they affect the following vehicles more than the other maneuvers. On top of this

the percentage of tracks which make the load/unload maneuver in this site is higher and

there exist two last zones.


As is it seen in Figure 5.30 above, the space mean speed gets reduced with the load/unload

vehicles. The space mean speed with the presence of load/unload vehicles is 27.65 km/hr.

This gives a significant speed reduction due to the load/unload vehicles. One significant

thing that can also be seen from this figure is that, at 40 meter distance the speed drops

significantly. And at the 90 meter distance also the speed drops for the same reason. On the

road geometry, these locations are where the last zones occur. As it was already mentioned

the specialty of this maneuver is the entire maneuvers take place at a specific location.

Therefore the averages of all the impacts also manifest this fact.

0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120

Sp

ace

Me

an

Sp

ee

d(K

m/h

r)

Space(meter)

Impact of Load/Unload on Speed

Free flowing cars

Private cars



62

5.2 Macro Analysis

5.2.1 Evaluation of the Regression Analysis

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.72207357

R Square 0.52139025

Adjusted R

Square

0.51533189

Standard Error 6.85785578

Observations 481

ANOVA

df SS MS F Significance F

Regression 6 24284.90412 4047.5 86.061 1.11493E-72

Residual 474 22292.30809 47.03

Total 480 46577.21221

Coefficients Standard

Error

t Stat P-value Lower 95% Upper 95%

Intercept 28.1415368 1.889784371 14.891 2E-41 24.42814589 31.85492771

Flow -0.0920736 0.028586033 -3.221 0.0014 -

0.148244628

-0.03590259

Parking

maneuver

-1.8119706 0.256809859 -7.056 6E-12 -

2.316597189

-

1.307344056

double

parking

-6.7303815 0.898768213 -7.488 3E-13 -8.49644418 -

4.964318751

load/unload

maneuvers

-2.8149148 1.078568394 -2.61 0.0093 -4.93428154 -0.69554813

no. Of lanes 13.5044132 0.823861342 16.392 4E-48 11.88554113 15.12328536

Shock wave

effect

-1.5032005 0.731700258 -2.054 0.0405 -

2.940977815

-

0.065423193

Table 5.3: Results of the regression analysis

According to the summary above, flow reduces the speed by 0.09. Similarly the parking

maneuvers, which comprise the front inbound, the reverse inbound and the out bound,

reduce the average speed by 1.81 km/hr. The double parking condition also reduces the

average speed by 6.73 km/hr. Similarly the load/unload maneuvers which are special

maneuvers at a specified location will also reduce the space mean speed by 2.82km/hr. On

the contrary the number of lanes increases the speed by 13.5Km/hr.



63

5.2.2 Evaluation of the Initial Hypotheses

According to the initial hypotheses:

1. The sign of flow, maneuvers, double parking and load/unload maneuvers are

expected to be negative. As it can be seen on the model all the mentioned variables

have a negative coefficient. This validates the initial hypothesis.

2. The sign of number of lanes was expected to be positive. As it can be seen on the

model those variables have a positive coefficient.

3. The coefficient of double parking was expected to be significantly higher than the

parking maneuvers and load /unload maneuvers. And the result affirms this

hypothesis.

4. As per the expectation the intercept has a positive sign.

Besides the initial assumptions there are some statistical parameters which indicate the

goodness of a model developed. One of these tests is t-statistics. According to Wikipedia, t-

statistics is a ratio of the departure of an estimated parameter from its notional value and

it’s standard error. It is used in hypothesis testing. Normally, the analysis was done with a

95% confidence interval. Therefore, the expected minimum t-stat value is ±1.95. Referring

to table 5.3, all the values are above the mentioned minimum value. And the corresponding

p-values are below 0.05.



64

Mean 35.94765331

Standard Error 0.449152259

Median 35.69321534

Mode #N/A

Standard

Deviation

9.850678087

Sample Variance 97.03585877

Kurtosis -0.297609726

Skewness 0.180871454

Range 47.70573139

Minimum 11.52409746

Maximum 59.22982885

Sum 17290.82124

Count 481

Table 5.4: Table for descriptive statistics

In most literatures there are some assumptions which lie behind a regression analysis. Some

of these assumptions are:

a. The distribution of the dependent variable should be Normal. There are various ways

to check the normality of a distribution; Jermy Miles and Mark Shevline put some

requirements on their book titled Applying Regression & Correlation; If the value of

skewness and kurtosis (only the magnitude) is greater than twice the standard error,

then the distribution is significantly different from normal distribution. The other

condition they put is that if the skewness statistic is less than 1 there should be low

problem. If the skewness is greater than 1 but less than 2, it should be emphasized

that there will be an effect on the parameter estimates, but it is probably ok. And

lastly if it is greater than 2, we should be concerned. As it can be seen from the

above descriptive statistics table the skewness and kurtosis are 0.1809 and -0.2976

respectively. And ignoring the signs, both these values are less than 1 and they are

also less than twice the standard error which is 0.9. Due to these facts it can be said

that the distribution of the dependent variables is Normal.

b. The other assumption is linearity. In a bivariate situation, the relationship between

the dependent and the independent variables should be linear. In a multivariate case

the value of the R2 is a good indication of the linearity. A value of R

2 very close to 1

indicates that we can make a very accurate (almost precise) prediction. But in

transportation studies it is always a big challenge to capture the impact of different

parameters. In addition to those variables which we assumed to affect the traffic

some other hidden factors could influence the analysis. This will sometimes result in

a deviated output. Due to those mentioned and other reasons an R2 value closely

near to 1 is not easy to attain. As it is given in the table above the R2 for this study is

0.52. There are 481 observations which could give enough variation.



65

c. The other assumption is homoscedasticity; the variance at every set of values for

independent variable is equal. The overall variance of the data was evaluated and

found to be more or less homoscedastic.

d. One of the assumptions is that there should be no correlation among the

independent variables. In most studies this assumption is the most difficult to

capture. Sometimes this assumption could be violated without any significant harm.

Most statistical software packages generate the correlation values.

speed Flow Parking

maneuver

double

parking

load/unload

maneuvers

no. Of

lanes

Shock

wave

effect

speed 1

Flow -0.2508 1

Parking

maneuver

-0.3609 0.0809 1

double parking -0.1478 0.0039 -0.03794 1

load/unload

maneuvers

-0.2197 0.0023 0.090865 0.034334 1

no. Of lanes 0.6068 -0.221 -0.19476 0.184765 -0.1717816 1

Shock wave

effect

-0.2126 0.02 0.178716 0.1557 0.1214420 -0.093 1

Table 5.5: Correlation

As it can seen from the above table most of the values are close to Zero except the

correlation between no. of lanes and speed. Normally this condition is a very challenging

phenomenon is many regression studies. It was tried to reduce the correlation, but it is not

easy to remove it.

But generally speaking, it can be said that the data used for the study is in a good shape.



66

6 Discussion In most of the studies impact of the presence of parked vehicles on the driving speed is analyzed.

But in this thesis work the impact of each category of parking maneuver and double parked vehicles

is studied. The result from both the micro and macro analysis of this study and the results from

different literatures are compared under this section.

According to Ibeas-Portilla, A._et_al (2009), there will be an increase in average journey time

up to by 15, 24 and 39% for 10, 20 and 30 maneuvers per hour respectively. And a reduction

in capacity of 6, 10 and 16% for maneuvers of 10, 20 and 30 per hour respectively.

According to Aronsson(2006) the presence of on-street parking has negative impact on the

space mean speed by (-5.54Km/hr). In a similar study by Nordström (2006) taking into

account mixed traffic in the city centers and applying linear regression model to estimate

the space mean speed as dependant variable the presence of on-street parking was found

to reduce the average speed by 3km/h. In both of these studies the intercept, which is the

free flow speed was found to be 40 and 45.

According to Wang et al (2006) the presence of on-street parking reduces the speed by 3.19

miles/h and the intercept in this case is 32.

With another approach Tivector(2009) studied the influence of on-street parking on the

average speed of urban street. On the study the streets were evaluated for one-sided and

two-sided on-street parking. For two-sided parking the speed reduction is 8.9km/h while for

one-sided it is 4.5km/h. And the intercept is 39.

The result from the three case studies of the micro analysis is tabulated below. In case study

one the number of reverse maneuvers is so less that it was not possible to get enough data

for the analysis. In the site selected for case study two there is no last zone. Therefore, the

load/unload maneuver for this case study is not included. In the last case study the position

of the bicycle lane is different from the other two case studies. It is situated between the

parking lane and the pedestrian sidewalk way. So the drivers barley double park in this site

and it was not possible to get enough data for the analysis of double parking.

Case study

one

Case study

two

Case study

three

Free flow 39.76 42 41

Inbound 32.38 34.07 33.06

reverse

inbound

- 32.8 33.4

Out bound 32 32.87 33.8

Double

Parking

35.74 31.6 -

Load/Unload 31.7 - 27.65

Table 6.1: The speed at different events



67

In the macro analysis, the free flow speed from the model is 41.6Km/hr. The speed

reductions from the parking maneuvers and double parking are 2Km/hr and 7Km/hr

respectively. In the same study the load/unload maneuvers caused a speed reduction of

3Km/hr.

7 Conclusions Many conclusions could be drawn from this study. But to put it in general words

parking maneuver is one of the numerous events that affect the traffic movement. In

relation to this event lots of disruptions could follow. According to this study the

maneuvers give rise to a speed reduction of almost 2Km/hr and double parking 7km/hr.

The load/unload maneuvers reduce the speed by 3Km/hr. This is a significant reduction.

In the course of this action deceleration and reacceleration will be experienced by many

vehicles which will lead to the release of more carbon dioxide and other gases to the

environment. This will have a significant environmental effect. Even though the aim of

this study is not accessing the environmental impact of parking maneuvers, it can be

taken as a lateral concern. Besides this due to the maneuvers the vehicles get delayed

on the average by 3 seconds. This delay is calculated in one block of the street due to a

single event. So for a reasonably long stretch there will be a significantly long delay. If

this delayed time is changed to monetary unit by calculating it with the value of time, it

will be a significant amount of road user cost. Therefore, implementing better parking

management strategies will help in saving the user cost.

The other point captured from this study is that the higher impact came from the

double parked vehicles, so providing alternative parking lots for the long duration

parking vehicles and permitting on-street parking only for short duration and delivery

vehicles could improve the performance of the street.

In the analysis it was also observed that among the inbound maneuvers the reverse

inbound maneuver happens to affect slightly more than the front inbound maneuvers.

And the out bounding maneuvers have a bit lesser impact than the other maneuvers.

The speed model developed is,

� � 28.14 � 0.09 � � 1.81 � � 6.73 �� 2.81 � ! � 1.5 #$ % 13.5 �&

Where,

� = Space speed



68

� = low

� = maneuvers

�� =double parking

� ! =load and unload

#$ �Shock wave effect

�&=number of lanes

From this model it can be seen that flow, maneuvers, double parking, the shock wave

effect and load/ unload have reductive effect with different magnitudes on the space

speed while number of lanes has an increasing effect. The free flow speed of this model

is calculated by keeping all the parameters zero and the number of lanes one. And it is

calculated to be 41Km/hr.

8 Further Study The subject matter which was tried to be covered by this study is so vast. It is not an

easy exercise to evaluate the impact of parking events only with this study. Due to the

limitation of time and data some interesting areas were not covered by this study.

The impact of parking maneuvers and double parking was studied only on the free

flowing vehicles. When there is a double parked vehicle, usually follows a queue which

originates from the double parked point. This phenomenon is called bottle neck. This

will lead to a bunching effect. The bunching vehicles could also be the focus of a study.

As it can be recognized from the moments the presence of bicycle lane helped in

reducing the impact the parking maneuver. Analyzing the impact of bicycle lanes in the

parking maneuver could also be other interesting area of study.

The sites selected are also routs of different buses which are part of the public transport

system. As it was mentioned in the introduction part of this study proper planning of the

parking activity will also help in the reliability of the transit system. So the impact of the

parking maneuvers could also be studied in relation to the public transport system.



69

9 Bibliography Childs, M.C (1999), Parking Spaces. A Design, Implementation and Use Manual for

Architects, Planners and Engineers. McGraw-Hill, US

Bång, K.L (1995), Highway Capacity Manual for Asian conditions. The first conference of

Eastern Asia Society for Transport Studies (EASTS). Manila, Philippines.

Bång, K.L (1995), Impact of side friction on speed-flow relationship for rural and urban

highways. SWEROAD, Indonesia.

Marsden, G.R (2006), The evidence base on parking policies-a review. Transport policy,

13(69, pp 447-457

Sisiopiko, V.p., Byrd, J.& Chittoor, A. (2007) Application of Level of Service Method for

the Evaluation of pedestrian Facilities, Paper No. 07-3150, TRB.

Highway Capacity Manual HCM 2010, Urban Street Segments-Chapter 17, National

cooperative Highway Research program, TRB. (Not published)

Ibeas-Portilla, A.el al(2009), Alonso-Orena, B.,Moura-Berodia, J.L, Ruisansanchez/Diaz,

F.J, Using M/M/∞ Queueing Model in On/Street parking Maneuvers, journal of

Transport Engineering, Volume 135, Issue 8, pp 527-535 (August 2009).

Shoup, D.C. (2006), Cruising for parking. Transport policy 13(2006) 479-486. ELSEVIER.

Aronsson, K.F.M (2006), Speed characteristics of urban streets based on driver behavior

studies and simulation. Doctorial Thesis in Infrastructure, Kungliga Tekniska Högskola,

Stockholm, Sweden.

Tivector AB(2009), Självförklarande gator-en pilotstudie. Rapport 2009:38 Version 1.0

AvSamprit Chatterjee, Ali S.Hadi (2006) Regression analysis by example

Marcel Buffat, (August 19, 2010) Perception of Urban Parking Problems, A survey among

parking experts in France (Maters Thesis)

Ibrahim Tunde Yusuf, Journal of American Science, 2010;6(12) The Factors For Free Flow

Speed On Urban Arterials – Empirical Evidences From Nigeria

Jermy Miles and Mark Shevline (2008) Applying Regression & Correlation, A Guide for

Students and Researchers.

Nordström, A.(2006), Hastighets-flödessamband för gator I tätortsmiljiö, Examenarbete,

Kungliga Tekniska Högskola, Stockholm, Sweden



70



71

Appendix 1: Table used for the Micro Analysis

Appendix 1.1: Table used for Case Study one

Appendix 1.1.1: Table used for the front inbound maneuver

Space Free flow

cum.

Travel time

Cum.

Average

travel time

Free flow

space mean

speed

Space

mean

speed

0 0 0

10 0.98352941 1.14 35.32547523 30.1907644

20 2.04117647 2.432 36.28526721 29.9002242

30 3.09411765 3.788 36.69525567 28.7736225

40 3.94470588 5.008 42.05375604 29.9278666

50 4.73882353 6.172 43.84919601 33.4890026

60 5.65176471 7.5 41.85048631 31.1393745

70 6.52705882 8.712 41.01211129 32.447172

80 7.42941176 9.952 40.87019818 33.2208273

90 8.29882353 11.056 41.39709782 34.7176063

100 9.17647059 12.076 40.99097577 36.4839776

110 10.1082353 13.204 38.73614957 33.6664156

120 11.1023529 14.352 38.07602464 34.5760205

Appendix1.1.2: Table used for out bound maneuver

Space Free flow

cum. Travel

time

Cum.

Average

travel time

Free flow

space mean

speed

Space

mean

speed

0 0 0

10 0.983529412 1.07 35.32547523 31.93384

20 2.041176471 2.34 36.28526721 30.36482

30 3.094117647 3.82 36.69525567 27.8778

40 3.944705882 5.43 42.05375604 29.79866

50 4.738823529 6.83 43.84919601 30.26248

60 5.651764706 8.32 41.85048631 29.88255

70 6.527058824 9.58 41.01211129 32.19764

80 7.429411765 10.83 40.87019818 31.57773

90 8.298823529 11.89 41.39709782 34.52105

100 9.176470588 12.9 40.99097577 35.91683

110 10.10823529 13.91 38.73614957 36.29123

120 11.10235294 15.08 38.07602464 33.34321



72

Appendix1.1.3: Table used for Double parking

Space Free flow

cum. Travel

time

Cum.

Average

travel

time

Free flow

space

mean

speed

Space

mean

speed

0 0 0

10 0.983529412 1.328 35.3254752 26.4937559

20 2.041176471 2.7 36.2852672 26.9104501

30 3.094117647 4.008 36.6952557 28.5736425

40 3.944705882 5.12 42.053756 32.1360663

50 4.738823529 6.172 43.849196 34.5734059

60 5.651764706 7.288 41.8504863 33.8147609

70 6.527058824 8.308 41.0121113 35.8123494

80 7.429411765 9.32 40.8701982 35.7687102

90 8.298823529 10.268 41.3970978 37.7927376

100 9.176470588 11.208 40.9909758 38.1090434

110 10.10823529 12.216 38.7361496 35.5115947

120 11.10235294 13.128 38.0760246 38.9373246

Appendix1.1.4: Table used for the Load/unload maneuver

Space Free flow

cum. Travel

time

Cum.

Average

travel time

Free flow

space mean

speed

Space mean

speed

0 0 0

10 0.983529412 1.306666667 35.32547523 26.36018371

20 2.041176471 2.56 36.28526721 30.04223805

30 3.094117647 3.84 36.69525567 29.77941176

40 3.944705882 5.053333333 42.05375604 29.55555556

50 4.738823529 6.213333333 43.84919601 32.47363636

60 5.651764706 7.866666667 41.85048631 28.64166667

70 6.527058824 9 41.01211129 32.36199095

80 7.429411765 10 40.87019818 36.83333333

90 8.298823529 10.93333333 41.39709782 38.6798419

100 9.176470588 11.96 40.99097577 35.38830585

110 10.10823529 13.13333333 38.73614957 32.19230769

120 11.10235294 15.14666667 38.07602464 27.95609962



73

Appendix 1.2: Table used for Case Study two


Space Free flow

cumulative

Time

Cum. Avg

travel time

Free flow

Space

mean

speed

Space

mean

speed

0 0 0

10 0.956 1.34 39.12 28.88

20 1.852 2.56 41.39 31.44

30 2.716 3.73 42.48 32.82

40 3.518 4.78 45.99 36.16

50 4.32 5.82 44.60 35.40

60 5.142 6.81 45.39 38.95

70 5.964 7.84 44.43 36.10

80 6.764 8.92 44.77 35.89

90 7.648 9.99 41.36 34.58

100 8.574 11.32 39.97 31.10

110 9.56 12.35 37.63 35.81

120 10.544 13.57 37.41 31.73

Appendix 1.2.2: Table used for the reverse inbound maneuver

Space Free flow

cumulative

Time

Cum. Avg

travel time

Free flow

Space mean

speed

Space mean

speed

0 0 0

10 0.96 1.17 39.12 31.94

20 1.85 2.76 41.39 31.28

30 2.72 4.41 42.48 29.28

40 3.52 5.64 45.99 31.74

50 4.32 6.87 44.60 30.53

60 5.14 7.97 45.39 33.32

70 5.96 9.06 44.43 34.43

80 6.76 10.13 44.77 35.42

90 7.65 11.14 41.36 35.98

100 8.57 12.24 39.97 33.94

110 9.56 13.38 37.63 33.12

120 10.54 14.51 37.41 32.62



74

Appendix 1.2.3: Table used for the outbound maneuver

Space Free flow

cumulative

Time

Cum. Avg

travel time

Free flow

Space

mean

speed

Space

mean

speed

0 0 0

10 0.96 1.14 39.12 34.24

20 1.85 2.20 41.39 34.47

30 2.72 3.28 42.48 35.02

40 3.52 4.34 45.99 35.04

50 4.32 5.38 44.60 35.32

60 5.14 6.52 45.39 33.54

70 5.96 7.57 44.43 36.81

80 6.76 8.73 44.77 31.95

90 7.65 9.97 41.36 31.77

100 8.57 11.53 39.97 27.57

110 9.56 12.81 37.63 30.28

120 10.54 14.17 37.41 28.46

Appendix 1.2.4: Table used for Double parking

Space Free flow

cumulative

Time

Cum. Avg

travel time

Free flow

Space

mean

speed

Space

mean

speed

0 0 0

10 0.96 1.33 39.12 28.50

20 1.85 2.58 41.39 29.68

30 2.72 3.83 42.48 29.33

40 3.52 4.96 45.99 33.07

50 4.32 6.12 44.60 32.05

60 5.14 7.28 45.39 31.87

70 5.96 8.35 44.43 34.09

80 6.76 9.39 44.77 34.93

90 7.65 10.61 41.36 31.49

100 8.57 11.84 39.97 31.64

110 9.56 12.94 37.63 33.35

120 10.54 14.25 37.41 29.17



75

Appendix 1.3: Table used for Case Study three


Space Free flow

cumulative

Time

Cum. Avg

travel

time

Free flow

Space

mean

speed

Space mean

speed

0 0 0

10 0.956 1.1957895 36.6141436 30.59576941

20 1.852 2.3452632 39.2208194 31.62819025

30 2.716 3.4968421 40.2588378 31.84009729

40 3.518 4.7115789 40.225155 30.32607055

50 4.32 5.8252632 41.5312057 31.78189894

60 5.142 7.0252632 41.4414651 32.33888961

70 5.964 8.0315789 42.5714881 34.49305984

80 6.764 9.1389474 42.9375897 34.98713463

90 7.648 10.185263 43.9960881 35.509425

100 8.574 11.162105 43.9556367 36.37331934

110 9.56 12.336842 39.8730979 33.81240505

Appendix 1.3.2: Table used for the reverse inbound maneuver

Space Free flow

cumulative

Time

Cum. Avg

travel

time

Free flow

Space mean

speed

Space

mean

speed

0 0 0

10 0.956 1.116 36.61414362 33.0671584

20 1.852 2.22 39.22081938 33.5850314

30 2.716 3.288 40.25883783 34.4343347

40 3.518 4.404 40.22515496 32.9917563

50 4.32 5.408 41.53120574 35.0447723

60 5.142 6.556 41.44146507 34.2898692

70 5.964 7.652 42.57148811 33.6933121

80 6.764 9.408 42.93758971 33.2860649

90 7.648 11.584 43.99608809 30.8340862

100 8.574 12.8 43.95563674 34.3474603

110 9.56 14.076 39.87309791 32.068775



76

Appendix 1.3.3: Table used for the outbound maneuver

Space Free flow

cumulative

Time

Cum. Avg

travel time

Free flow

Space mean

speed

Space

mean

speed

0 0 0

10 0.956 1.24727273 36.61414362 30.30923

20 1.852 2.41090909 39.22081938 32.06269

30 2.716 3.53090909 40.25883783 33.00371

40 3.518 4.67272727 40.22515496 32.37902

50 4.32 5.70909091 41.53120574 33.90502

60 5.142 6.85454545 41.44146507 34.10743

70 5.964 7.88727273 42.57148811 34.43526

80 6.764 8.98545455 42.93758971 36.15187

90 7.648 10.0218182 43.99608809 36.03693

100 8.574 11.0036364 43.95563674 36.75617

110 9.56 12.2218182 39.87309791 32.58933

Appendix1.3.4: Table used for the Load/unload maneuver

Space Free flow

cumulative

Time

Cum.

Avg

travel

time

Free flow

Space mean

speed

Space

mean

speed

0 0 0

10 0.956 1.6 36.61414362 24.4414

20 1.852 3.354545 39.22081938 23.50119

30 2.716 5.434545 40.25883783 21.27325

40 3.518 7.638182 40.22515496 18.55775

50 4.32 9.145455 41.53120574 23.78489

60 5.142 10.53636 41.44146507 27.22008

70 5.964 11.67273 42.57148811 30.03404

80 6.764 12.77636 42.93758971 34.70085

90 7.648 13.88909 43.99608809 32.98195

100 8.574 14.87818 43.95563674 36.25949

110 9.56 16.14364 39.87309791 31.33963



77

Appendix 2: the table used for the regression analysis

Start

Time

End

Time

travel

time

speed Flow Parking

maneuver

double

parking

load/unload

maneuvers

no.

Of

lanes

Shock

wave

effect

7:05 7:10 8.782667 57.39 41 0 0 0 1 0

7:10 7:15 10 50.40 46 0 0 0 1 0

7:15 7:20 10.51385 47.94 49 2 0 0 1 0

7:20 7:25 10.7125 47.05 51 1 0 0 1 1

7:25 7:30 10.8575 46.42 52 1 0 0 1 1

7:30 7:35 11.61091 43.41 41 2 0 0 1 1

7:35 7:40 9.756923 51.66 53 0 0 0 1 1

7:40 7:45 10.42 48.37 40 1 0 0 1 0

7:45 7:50 11.16 45.16 51 0 0 0 1 1

7:50 7:55 10.9856 45.88 58 1 0 0 1 0

7:55 8:00 11.38783 44.26 60 1 0 0 1 1

8:00 8:05 12.56 40.13 52 2 0 0 1 1

8:05 8:10 10.50667 47.97 56 1 0 0 1 1

8:10 8:15 12.56267 40.12 56 2 0 0 1 1

8:15 8:20 11.89067 42.39 51 3 0 0 1 1

8:20 8:25 11.22182 44.91 69 1 0 0 1 1

8:25 8:30 11.72 43.00 47 0 0 1 1 1

8:30 8:35 12.87158 39.16 45 0 1 0 1 1

8:35 8:40 10.87714 46.34 64 0 0 0 1 1

8:40 8:45 12.68267 39.74 44 2 0 0 1 0

8:45 8:50 11.86 42.50 45 4 0 0 1 1

8:50 8:55 12.7232 39.61 64 1 0 1 1 1

8:55 9:00 12.21455 41.26 47 2 0 0 1 1

9:00 9:05 12.86154 39.19 52 2 0 0 1 1

9:05 9:10 13.73263 36.70 61 4 0 0 1 1

9:10 9:15 11.11667 45.34 57 0 0 0 1 1

9:15 9:20 12.02667 41.91 47 3 0 0 1 0

9:20 9:25 12.39333 40.67 56 2 0 0 1 1

9:25 9:30 9.588889 52.56 53 0 0 0 1 1

9:30 9:35 12.33714 40.85 48 2 0 0 1 0

9:35 9:40 12.74353 39.55 57 4 0 0 1 1

9:40 9:45 13.592 37.08 47 5 0 0 1 1

9:45 9:50 12.6592 39.81 51 2 0 0 1 1

9:50 9:55 10.97935 45.90 53 0 0 0 1 1

9:55 10:00 9.276 54.33 52 0 0 0 1 0

10:00 10:05 12.398 40.65 36 2 0 0 1 0

10:05 10:10 10.29455 48.96 45 0 0 0 1 1

10:10 10:15 13.56516 37.15 60 1 0 0 1 0

10:15 10:20 13.36148 37.72 46 1 1 0 1 1



78

10:20 10:25 12.745 39.54 54 3 0 0 1 1

10:25 10:30 10.395 48.48 63 0 0 0 1 1

10:50 10:55 10.38 48.55 62 0 0 0 1 0

10:55 11:00 13.516 37.29 50 2 0 0 1 0

11:00 11:05 13.0725 38.55 56 2 0 0 1 1

11:05 11:10 13.42364 37.55 45 3 0 0 1 1

11:10 11:15 13.25 38.04 54 2 0 0 1 1

11:15 11:20 12.04 41.86 53 2 0 0 1 1

11:20 11:25 10.288 48.99 37 0 0 0 1 1

11:25 11:30 13.968 36.08 37 2 0 0 1 0

13:30 13:35 10.19385 49.44 47 0 0 0 1 1

13:35 13:40 14.81053 34.03 55 3 0 0 1 0

13:40 13:45 14.1875 35.52 47 2 1 0 1 1

13:45 13:50 14.88 33.87 53 1 1 0 1 1

13:50 13:55 10.25333 49.15 54 0 0 0 1 1

13:55 14:00 12.06909 41.76 54 2 0 0 1 0

14:00 14:05 12.23765 41.18 43 2 0 0 1 1

14:05 14:10 12.546 40.17 50 3 0 0 1 1

14:10 14:15 10.28 49.03 54 0 0 0 1 1

14:15 14:20 11.31333 44.55 47 1 0 0 1 0

14:20 14:25 12.69 39.72 52 0 0 0 1 1

14:25 14:30 12.58 40.06 56 0 0 0 1 0

14:30 14:35 12.3675 40.75 45 1 0 0 1 0

14:35 14:40 15.67636 32.15 48 2 0 0 1 1

14:40 14:45 13.32571 37.82 61 5 0 0 1 1

14:45 14:50 13.4925 37.35 52 3 0 0 1 1

14:50 14:55 10.76727 46.81 51 0 0 0 1 1

14:55 15:00 12.69333 39.71 63 0 0 0 1 0

15:00 15:05 9.766 51.61 41 0 0 0 1 0

15:05 15:10 10.03333 50.23 50 0 0 0 1 0

15:10 15:15 9.285714 54.28 63 0 0 0 1 0

15:15 15:20 12.17 41.41 57 0 1 0 1 0

15:20 15:25 13 38.77 61 1 0 0 1 1

15:25 15:30 12.09667 41.66 69 1 0 0 1 1

15:30 15:35 11.49111 43.86 56 1 0 0 1 1

15:35 15:40 23.13417 21.79 59 3 1 0 1 1

15:40 15:45 29.44 17.12 72 0 0 1 1 1

15:45 15:50 34.2864 14.70 62 3 0 0 1 1

15:55 16:00 21.06667 23.92 55 0 1 0 1 1

16:05 16:10 18.34 27.48 45 6 0 0 1 1

16:10 16:15 30.42667 16.56 60 1 0 0 1 1

16:30 16:35 14.775 34.11 48 3 0 0 1 1

16:40 16:45 28.92857 17.42 65 7 1 1 1 1

16:45 16:50 18.288 27.56 48 2 0 1 1 1

16:50 16:55 12.448 40.49 54 2 0 0 1 1

16:55 17:00 14.595 34.53 68 0 0 0 1 1



79

7:05 7:10 10.44 41.39 44 1 0 0 1 0

7:10 7:15 11.31 38.18 37 0 0 0 1 1

7:15 7:20 11.78 36.66 49 1 0 0 1 0

7:20 7:25 13.15 32.85 39 1 0 0 1 1

7:25 7:30 12.07 35.79 42 1 0 0 1 1

7:30 7:35 12.48 34.60 47 3 0 0 1 1

7:35 7:40 14.46 29.87 69 3 0 0 1 1

7:40 7:45 15.09 28.63 49 1 0 0 1 1

7:45 7:50 12.35 34.99 53 5 0 0 1 1

7:50 7:55 13.93 31.00 45 3 0 0 1 1

7:55 8:00 14.45 29.89 46 2 0 0 1 1

8:00 8:05 13.74 31.44 60 5 0 0 1 1

8:05 8:10 18.21 23.73 62 1 0 0 1 1

8:10 8:15 17.65 24.47 62 0 0 0 1 1

8:15 8:20 19.66 21.97 62 3 0 0 1 0

8:20 8:25 19.60 22.04 56 1 1 0 1 1

8:25 8:30 18.64 23.17 64 2 1 0 1 1

8:30 8:35 18.84 22.93 57 0 1 1 1 1

8:35 8:40 18.55 23.29 63 0 0 0 1 1

8:40 8:45 16.59 26.04 48 1 0 0 1 0

8:45 8:50 18.04 23.95 61 1 0 0 1 1

8:50 8:55 15.77 27.40 55 0 0 0 1 1

8:55 9:00 13.93 31.01 51 1 0 1 1 0

9:00 9:05 12.39 34.86 50 0 0 0 1 1

9:05 9:10 19.38 22.29 59 2 0 0 1 0

9:10 9:15 33.89 12.75 47 1 1 0 1 1

9:15 9:20 21.66 19.94 55 0 1 0 1 1

9:20 9:25 20.35 21.23 61 2 1 0 1 1

9:25 9:30 14.01 30.84 58 4 0 1 1 1

9:30 9:35 17.40 24.83 54 1 0 0 1 1

9:35 9:40 21.85 19.77 58 0 0 1 1 1

9:40 9:45 23.53 18.36 70 0 0 0 1 1

9:45 9:50 20.27 21.32 62 0 0 0 1 0

9:50 9:55 17.47 24.73 61 1 0 0 1 0

9:55 10:00 16.87 25.60 67 2 1 1 1 1

10:00 10:05 20.56 21.02 54 3 1 1 1 1

10:05 10:10 18.46 23.40 69 0 0 0 1 1

10:10 10:15 20.88 20.69 55 3 1 1 1 0

10:15 10:20 14.49 29.82 45 3 1 0 1 1

10:20 10:25 20.94 20.63 56 2 1 1 1 1

10:30 10:35 24.21 17.84 67 1 0 0 1 1

10:35 10:40 16.84 25.65 61 1 0 0 1 1

10:40 10:45 14.99 28.82 50 0 0 0 1 1

10:45 10:50 17.30 24.97 60 4 0 0 1 0

10:50 10:55 17.41 24.82 60 2 0 0 1 1

10:55 11:00 16.84 25.66 52 1 0 0 1 1



80

11:00 11:05 16.82 25.69 47 1 0 0 1 1

11:05 11:10 15.63 27.64 52 1 0 0 1 1

11:10 11:15 23.38 18.48 62 2 0 0 1 1

11:15 11:20 12.91 33.46 49 3 1 0 1 1

11:20 11:25 16.83 25.67 56 1 1 0 1 1

11:25 11:30 14.17 30.48 45 3 0 0 1 1

13:30 13:35 15.53 27.82 45 1 0 0 1 1

13:35 13:40 18.27 23.64 49 0 0 0 1 1

13:45 13:50 17.37 24.88 53 1 0 0 1 0

13:50 13:55 14.85 29.08 52 1 0 0 1 1

13:55 14:00 11.89 36.33 48 0 0 0 1 1

14:00 14:05 15.51 27.86 50 1 0 0 1 0

14:05 14:10 14.71 29.37 47 1 1 0 1 1

14:10 14:15 19.70 21.93 57 1 0 0 1 1

14:20 14:25 26.47 16.32 57 1 0 0 1 1

14:25 14:30 20.58 21.00 58 2 0 0 1 1

14:35 14:40 14.88 29.03 46 4 1 0 1 1

14:40 14:45 37.49 11.52 48 1 1 0 1 1

14:45 14:50 28.82 14.99 54 2 0 0 1 1

14:55 15:00 16.02 26.96 52 0 0 0 1 1

15:00 15:05 22.36 19.32 50 1 0 0 1 0

15:05 15:10 22.75 18.99 62 1 1 0 1 1

15:10 15:15 20.23 21.35 57 3 0 0 1 1

15:15 15:20 27.73 15.58 58 3 0 0 1 1

15:20 15:25 17.96 24.06 41 1 0 0 1 1

15:25 15:30 17.56 24.61 56 4 0 0 1 1

15:30 15:35 17.06 25.32 49 4 0 0 1 1

15:35 15:40 17.22 25.09 54 3 0 0 1 1

15:40 15:45 21.27 20.32 58 1 0 0 1 1

15:45 15:50 19.59 22.05 58 1 0 0 1 1

15:55 16:00 19.92 21.69 56 0 1 1 1 1

16:00 16:05 19.37 22.31 46 2 1 0 1 1

16:05 16:10 15.06 28.68 56 2 0 1 1 1

16:10 16:15 17.38 24.85 45 2 0 0 1 1

16:15 16:20 17.69 24.42 47 4 0 0 1 1

16:20 16:25 14.96 28.88 47 4 0 0 1 1

16:25 16:30 14.59 29.60 44 1 0 0 1 1

16:30 16:35 15.85 27.25 50 3 0 0 1 1

16:35 16:40 28.43 15.20 57 1 0 0 1 1

16:40 16:45 23.25 18.58 56 2 0 0 1 1

16:45 16:50 36.74 11.76 58 1 0 0 1 1

16:50 16:55 27.35 15.79 47 2 0 0 1 1

16:55 17:00 20.01 21.59 62 3 0 0 1 1

17:00 17:05 29.77 14.51 50 1 0 0 1 1

17:05 17:10 19.19 22.51 52 3 0 0 1 1

17:10 17:15 16.16 26.73 46 1 0 0 1 1



81

17:15 17:20 18.45 23.42 46 0 0 0 1 1

17:20 17:25 19.01 22.73 53 2 0 0 1 0

17:25 17:30 17.50 24.69 53 3 0 0 1 1

7:05 7:10 4.89 36.78 40 0 0 0 1 1

7:10 7:15 5.23 34.42 31 0 0 0 1 0

7:15 7:20 4.72 38.14 37 0 0 0 1 0

7:20 7:25 4.80 37.52 23 0 0 0 1 0

7:25 7:30 4.70 38.28 35 0 0 0 1 0

7:30 7:35 4.71 38.23 34 0 0 0 1 0

7:35 7:40 4.51 39.94 44 0 0 0 1 0

7:40 7:45 4.60 39.09 49 0 0 0 1 0

7:45 7:50 4.73 38.08 35 0 0 0 1 0

7:50 7:55 4.52 39.82 46 0 0 0 1 0

7:55 8:00 4.73 38.07 33 0 0 0 1 0

8:00 8:05 5.61 32.06 53 1 0 0 1 0

8:05 8:10 6.27 28.71 30 1 0 0 1 1

8:10 8:15 4.76 37.84 29 0 0 0 1 1

8:15 8:20 5.35 33.62 60 0 0 0 1 0

8:20 8:25 5.94 30.29 55 1 0 0 1 0

8:25 8:30 5.39 33.37 44 0 0 0 1 1

8:30 8:35 4.76 37.82 38 0 0 0 1 0

8:35 8:40 6.47 27.83 45 2 0 0 1 0

8:40 8:45 5.85 30.75 35 1 0 0 1 1

8:45 8:50 6.31 28.53 35 1 0 0 1 1

8:50 8:55 4.87 36.97 33 0 0 0 1 1

8:55 9:00 6.09 29.57 40 1 0 0 1 0

9:00 9:05 6.31 28.52 33 2 0 0 1 1

9:05 9:10 4.54 39.63 39 0 0 0 1 1

9:10 9:15 6.33 28.45 44 2 0 0 1 0

9:15 9:20 5.13 35.10 51 0 0 0 1 1

9:20 9:25 5.97 30.15 40 1 0 0 1 0

9:25 9:30 5.78 31.14 48 1 0 0 1 1

9:30 9:35 4.59 39.22 34 0 0 0 1 1

9:35 9:40 4.47 40.30 44 0 0 0 1 0

9:40 9:45 4.51 39.90 37 0 0 0 1 0

9:45 9:50 5.43 33.14 40 0 0 0 1 0

9:50 9:55 5.18 34.77 32 0 0 0 1 0

9:55 10:00 4.36 41.25 43 0 0 0 1 0

10:00 10:05 6.36 28.32 35 3 0 0 1 0

10:05 10:10 6.53 27.55 23 1 0 0 1 1

10:10 10:15 5.73 31.41 35 1 0 0 1 1

10:15 10:20 4.93 36.52 28 0 0 0 1 1

10:20 10:25 6.47 27.80 36 1 0 0 1 0

10:25 10:30 6.19 29.07 36 1 0 0 1 1

10:30 10:35 5.68 31.69 20 2 1 0 1 1

10:35 10:40 4.66 38.59 44 0 0 0 1 1



82

10:40 10:45 7.34 24.51 38 1 1 0 1 0

10:45 10:50 6.24 28.85 33 1 0 0 1 1

10:50 10:55 6.47 27.84 28 0 1 0 1 1

10:55 11:00 6.11 29.46 34 1 1 0 1 1

11:00 11:05 6.27 28.69 33 0 1 0 1 1

11:05 11:10 6.14 29.33 33 0 1 0 1 1

11:10 11:15 7.10 25.34 32 2 0 0 1 1

11:15 11:20 4.60 39.13 27 0 0 0 1 1

11:20 11:25 7.16 25.13 33 3 0 0 1 0

11:25 11:30 4.93 36.49 32 0 0 0 1 1

11:30 11:35 4.79 37.57 32 0 0 0 1 0

13:25 13:30 5.84 30.82 41 1 0 0 1 0

13:30 13:35 6.27 28.69 36 3 0 0 1 1

13:35 13:40 6.36 28.31 46 0 1 1 1 1

13:40 13:45 6.78 26.53 34 3 1 0 1 1

13:45 13:50 6.29 28.63 41 2 0 0 1 1

13:50 13:55 6.36 28.31 40 0 1 0 1 1

13:55 14:00 6.68 26.95 30 1 1 0 1 1

14:00 14:05 6.81 26.43 22 2 0 0 1 1

14:05 14:10 6.08 29.59 33 3 0 0 1 1

14:10 14:15 5.13 35.07 29 0 0 0 1 1

14:15 14:20 6.24 28.85 43 1 1 0 1 0

14:20 14:25 5.89 30.56 43 1 0 0 1 1

14:25 14:30 6.35 28.36 33 2 0 0 1 1

14:30 14:35 7.64 23.57 29 3 0 0 1 1

14:35 14:40 6.61 27.24 41 2 0 0 1 1

14:40 14:45 7.16 25.15 39 4 1 0 1 1

14:45 14:50 6.47 27.83 45 2 1 1 1 1

14:50 14:55 6.98 25.80 39 2 1 0 1 1

14:55 15:00 5.76 31.27 43 1 0 0 1 1

15:00 15:05 4.70 38.34 38 0 0 0 1 1

15:05 15:10 4.82 37.38 31 0 0 0 1 0

15:10 15:15 6.79 26.52 43 1 0 1 1 0

15:15 15:20 6.58 27.35 34 0 0 1 1 1

15:20 15:25 6.28 28.68 40 1 1 0 1 1

15:25 15:30 6.28 28.67 34 1 0 1 1 1

15:30 15:35 4.96 36.30 45 0 0 0 1 1

15:35 15:40 4.69 38.42 48 0 0 0 1 0

15:40 15:45 6.11 29.45 49 2 0 0 1 0

15:45 15:50 6.46 27.87 42 1 0 0 1 1

15:50 15:55 6.38 28.21 46 0 0 1 1 1

15:55 16:00 6.55 27.47 42 3 0 0 1 1

16:00 16:05 5.64 31.90 36 1 0 0 1 1

16:05 16:10 6.12 29.40 33 2 0 0 1 1

16:10 16:15 5.87 30.67 43 1 0 0 1 1

16:15 16:20 6.10 29.49 35 1 0 1 1 1



83

16:20 16:25 6.19 29.09 32 2 0 0 1 1

16:25 16:30 4.84 37.19 40 0 0 0 1 1

16:30 16:35 5.13 35.08 48 0 0 0 1 0

16:35 16:40 5.03 35.77 35 0 0 0 1 0

16:40 16:45 4.85 37.14 49 0 0 0 1 0

16:45 16:50 6.63 27.15 40 1 0 0 1 0

16:50 16:55 6.40 28.11 27 1 1 0 1 1

16:55 17:00 4.92 36.61 37 0 0 0 1 1

17:00 17:05 5.42 33.21 43 0 0 0 1 0

17:05 17:10 4.88 36.91 46 0 0 0 1 0

17:10 17:15 4.82 37.31 44 0 0 0 1 0

17:15 17:20 5.85 30.77 35 1 0 0 1 0

17:20 17:25 6.06 29.68 46 0 0 1 1 1

17:25 17:30 6.30 28.55 29 1 0 1 1 1

7:05 7:10 5.168 59.21 40 0 0 0 2 1

7:10 7:15 6.097143 50.19 31 0 0 0 2 0

7:15 7:20 5.166316 59.23 37 0 0 0 2 0

7:20 7:25 5.946667 51.46 23 1 0 0 2 0

7:25 7:30 5.418462 56.47 35 0 0 0 2 1

7:30 7:35 7.08 43.22 34 1 0 0 2 0

7:35 7:40 7.297143 41.93 44 1 0 0 2 1

7:40 7:45 5.903529 51.83 49 1 0 0 2 1

7:45 7:50 6.176842 49.54 35 0 1 0 2 1

7:50 7:55 5.6775 53.90 46 0 0 0 2 1

7:55 8:00 6.857143 44.63 33 1 0 0 2 0

8:00 8:05 6.375 48.00 53 1 0 0 2 1

8:05 8:10 5.466667 55.98 30 0 0 0 2 1

8:10 8:15 5.32 57.52 29 0 0 0 2 0

8:15 8:20 5.54 55.23 60 0 0 0 2 0

8:20 8:25 5.626667 54.38 55 0 0 0 2 0

8:25 8:30 5.868 52.15 44 0 0 0 2 0

8:30 8:35 5.3175 57.55 38 0 0 0 2 0

8:35 8:40 7.634286 40.08 45 0 0 0 2 0

8:40 8:45 6.383333 47.94 35 1 0 0 2 0

8:45 8:50 7.556 40.50 35 1 0 0 2 1

8:50 8:55 7.131429 42.91 33 0 1 0 2 1

8:55 9:00 7.392941 41.39 40 3 0 0 2 1

9:00 9:05 5.793846 52.81 33 0 0 0 2 1

9:05 9:10 5.46 56.04 39 0 0 0 2 0

9:10 9:15 7.522667 40.68 44 1 0 0 2 0

9:15 9:20 6.742222 45.39 51 2 0 0 2 1

9:20 9:25 5.54 55.23 40 0 0 0 2 1

9:25 9:30 5.861333 52.21 48 0 0 0 2 0

9:30 9:35 7.484444 40.88 34 1 0 0 2 0

9:35 9:40 6.944 44.07 44 4 0 0 2 1

9:40 9:45 5.301538 57.72 37 0 0 0 2 1



84

9:45 9:50 6.859048 44.61 40 1 0 0 2 0

9:50 9:55 6.966667 43.92 32 2 0 0 2 1

9:55 10:00 7.235789 42.29 43 0 1 0 2 1

10:00 10:05 7.061538 43.33 35 1 1 0 2 1

10:05 10:10 7.526154 40.66 23 5 0 0 2 1

10:10 10:15 6.582857 46.48 35 2 1 0 2 1

10:15 10:20 5.329231 57.42 28 0 0 0 2 1

10:20 10:25 7.4 41.35 36 2 1 0 2 0

10:25 10:30 6.678621 45.82 36 1 0 0 2 1

10:30 10:35 6.733333 45.45 20 1 0 0 2 1

10:35 10:40 6.676364 45.83 44 1 0 0 2 1

10:40 10:45 8.6 35.58 38 0 1 0 2 1

10:45 10:50 7.4575 41.03 33 0 1 0 2 1

10:50 10:55 8.374545 36.54 28 1 1 0 2 1

10:55 11:00 7.7 39.74 34 1 1 0 2 1

11:00 11:05 7.525161 40.66 33 0 1 0 2 1

11:05 11:10 7.395 41.38 33 0 1 0 2 1

11:10 11:15 7.374737 41.49 32 0 1 0 2 1

11:15 11:20 6.713333 45.58 27 0 1 0 2 1

11:20 11:25 7.273333 42.07 33 0 1 0 2 1

11:25 11:30 7.249655 42.21 32 0 1 0 2 1

11:30 11:35 6.98 43.84 32 0 1 0 2 1

13:25 13:30 6.548235 46.73 41 1 0 0 2 1

13:30 13:35 5.384 56.84 36 0 0 0 2 1

13:35 13:40 5.315556 57.57 46 0 0 0 2 0

13:40 13:45 7.004444 43.69 34 0 1 0 2 0

13:45 13:50 6.046667 50.61 41 0 0 0 2 1

13:50 13:55 7.271111 42.08 40 1 0 0 2 0

13:55 14:00 5.985455 51.12 30 0 0 0 2 1

14:00 14:05 5.71 53.59 22 0 0 0 2 0

14:05 14:10 5.773333 53.00 33 0 0 0 2 0

14:10 14:15 7.376842 41.48 29 0 1 0 2 0

14:15 14:20 7.248 42.22 43 2 1 0 2 1

14:20 14:25 7.681333 39.84 43 1 1 0 2 1

14:25 14:30 7.36 41.58 33 2 1 0 2 1

14:30 14:35 6.333333 48.32 29 0 0 0 2 1

14:35 14:40 7.096667 43.12 41 1 0 0 2 0

14:40 14:45 7.742857 39.52 39 2 1 0 2 1

14:45 14:50 7.36 41.58 45 0 1 0 2 1

14:50 14:55 7.330526 41.74 39 1 1 0 2 1

14:55 15:00 7.098571 43.11 43 1 1 0 2 1

15:00 15:05 5.208571 58.75 38 0 0 0 2 1

15:05 15:10 5.4075 56.59 31 0 0 0 2 0

15:10 15:15 7.211429 42.43 43 2 0 0 2 0

15:15 15:20 5.935 51.56 34 0 0 0 2 1

15:20 15:25 7.506667 40.76 40 0 1 0 2 0



85

15:25 15:30 7.044 43.44 34 2 0 0 2 1

15:30 15:35 5.36381 57.05 45 0 0 0 2 1

15:35 15:40 6.069412 50.42 48 1 0 0 2 0

15:40 15:45 6.019487 50.83 49 1 0 0 2 1

15:45 15:50 7.332632 41.73 42 1 1 0 2 1

15:50 15:55 7.64 40.05 46 3 0 0 2 1

15:55 16:00 5.61 54.55 42 0 0 0 2 1

16:00 16:05 6.82 44.87 36 2 0 0 2 0

16:05 16:10 5.345 57.25 33 0 0 0 2 1

16:10 16:15 5.315 57.57 43 0 0 0 2 0

16:15 16:20 5.63 54.35 35 0 0 0 2 0

16:20 16:25 6.782222 45.12 32 2 0 0 2 0

16:25 16:30 6.930909 44.15 40 1 0 0 2 1

16:30 16:35 6.694545 45.71 48 1 0 0 2 1

16:35 16:40 6.57 46.58 35 1 0 0 2 1

16:40 16:45 5.36 57.09 49 0 0 0 2 1

16:45 16:50 5.276364 57.99 40 0 0 0 2 0

16:50 16:55 6.779048 45.14 27 1 0 0 2 0

16:55 17:00 7.113333 43.02 37 1 0 0 2 1

17:00 17:05 7.078 43.23 43 1 1 0 2 1

17:05 17:10 7.888 38.79 46 4 0 0 2 1

17:10 17:15 7.013333 43.63 44 1 0 0 2 1

17:15 17:20 5.348571 57.21 35 0 0 0 2 1

17:20 17:25 6.976 43.86 46 3 0 0 2 0

17:25 17:30 7.335385 41.72 29 3 0 0 2 1

7:35 7:40 9.628571 41.127596 28 0 0 0 1 1

7:40 7:45 10.555 37.517764 24 4 0 0 1 0

7:45 7:50 14.62 27.086183 23 2 0 1 1 1

7:50 7:55 10.35 38.26087 21 1 0 0 1 1

7:55 8:00 10.60727 37.332876 20 1 0 0 1 1

8:00 8:05 14.27333 27.744045 21 2 0 0 1 1

8:05 8:10 11.59636 34.148636 22 1 0 0 1 1

8:10 8:15 9.693333 40.85282 31 0 0 0 1 1

8:15 8:20 12.92667 30.634348 24 1 0 1 1 0

8:20 8:25 9.248 42.820069 23 0 0 0 1 1

8:25 8:30 11.92923 33.19577 35 2 0 1 1 0

8:30 8:35 11.992 33.022015 28 3 0 0 1 1

8:35 8:40 11.33231 34.944339 37 3 0 0 1 1

8:40 8:45 11.09455 35.693215 38 1 0 1 1 1

8:45 8:50 10.54 37.571157 21 2 0 0 1 1

8:50 8:55 10.32 38.372093 19 1 0 0 1 1

8:55 9:00 9.668 40.959868 29 0 0 0 1 1

9:00 9:05 11.24 35.231317 22 3 0 0 1 0

9:05 9:10 12.72364 31.123178 24 2 0 0 1 1

9:10 9:15 11.28 35.106383 22 1 0 0 1 1

9:15 9:20 10.97455 36.083499 28 1 0 0 1 1



86

9:20 9:25 10.956 36.144578 27 3 0 0 1 1

9:25 9:30 9.36 42.307692 33 0 0 0 1 1

9:30 9:35 9.85 40.203046 35 0 0 0 1 0

9:35 9:40 9.470625 41.813502 33 0 0 0 1 0

9:40 9:45 9.12 43.421053 20 0 0 0 1 0

9:45 9:50 10.456 37.872992 32 1 0 0 1 0

9:50 9:55 13.57231 29.177057 27 1 0 0 1 1

9:55 10:00 11.33455 34.93744 33 2 0 0 1 1

10:00 10:05 12.02 32.945092 33 2 0 0 1 1

10:05 10:10 11.88 33.333333 26 3 0 1 1 1

10:10 10:15 12.16364 32.556054 35 2 0 1 1 1

10:15 10:20 11.95273 33.130514 30 3 0 0 1 1

10:20 10:25 15.65333 25.298126 24 6 0 0 1 1

10:25 10:30 11.945 33.151946 26 1 0 0 1 1

10:30 10:35 11.49333 34.454756 28 0 0 1 1 1

10:35 10:40 12.28 32.247557 29 2 0 0 1 1

10:40 10:45 13.74545 28.809524 33 3 0 1 1 1

10:45 10:50 12.71467 31.145134 29 1 0 1 1 1

10:50 10:55 10.956 36.144578 22 1 0 0 1 1

10:55 11:00 12.37818 31.991774 23 1 0 1 1 1

11:00 11:05 11.19636 35.368626 28 2 0 1 1 1

11:05 11:10 11.67733 33.911852 39 2 0 1 1 1

11:10 11:15 8.916667 44.411215 28 0 0 0 1 1

11:15 11:20 11.56 34.256055 37 1 0 0 1 0

11:20 11:25 15.305 25.873897 39 2 0 1 1 1

11:25 11:30 8.537143 46.385542 26 0 0 1 1 1

11:30 11:35 14.20889 27.869878 29 2 0 0 1 1

11:35 11:40 10.836 36.54485 20 2 0 0 1 1

11:40 11:45 12.28222 32.241722 38 4 0 0 1 1

11:45 11:50 11.58667 34.177215 25 2 0 0 1 1

11:50 11:55 12.34667 32.073434 40 3 0 0 1 1

13:55 14:00 12.12 32.673267 33 3 0 0 1 1

14:00 14:05 9.9 40 29 2 0 0 1 1

14:05 14:10 12.52364 31.620209 35 3 0 1 1 1

14:10 14:15 9.52 41.596639 36 0 0 0 1 1

14:15 14:20 11.66667 33.942857 18 3 0 0 1 0

14:20 14:25 11.44727 34.593393 42 1 0 0 1 1

14:25 14:30 11.2925 35.067523 29 1 0 0 1 1

14:30 14:35 9.403636 42.111369 29 0 0 0 1 1

14:35 14:40 11.352 34.883721 37 2 0 0 1 0

14:40 14:45 12.84286 30.83426 39 1 0 0 1 1

14:45 14:50 14.45 27.404844 42 1 0 1 1 1

14:50 14:55 12.42118 31.881038 43 3 0 1 1 1

14:55 15:00 12.72667 31.115767 38 3 0 0 1 1

15:00 15:05 13.624 29.066353 36 2 0 1 1 1

15:05 15:10 8.636923 45.849662 23 2 0 0 1 1



87

15:10 15:15 11.19385 35.376581 44 1 0 1 1 1

15:15 15:20 11.29333 35.064935 36 1 0 0 1 1

15:20 15:25 11.63636 34.03125 44 1 0 1 1 1

15:25 15:30 10.984 36.05244 38 3 0 1 1 1

15:30 15:35 13.55714 29.209694 40 1 0 1 1 1

15:35 15:40 16.884 23.454158 36 5 0 1 1 1

15:40 15:45 13.276 29.828262 39 2 0 0 1 1

15:45 15:50 12.7175 31.138195 41 1 0 0 1 1

15:50 15:55 11.9725 33.075799 49 1 0 0 1 1

15:55 16:00 9.488 41.736931 25 0 0 0 1 1

16:00 16:05 12.40842 31.913811 46 1 0 0 1 0

16:05 16:10 11.485 34.479756 32 2 0 0 1 1

16:10 16:15 11.86571 33.373465 35 2 0 0 1 1

16:15 16:20 11.592 34.161491 48 1 0 0 1 1

16:20 16:25 11.26133 35.164575 39 3 0 0 1 1

16:25 16:30 11.76 33.673469 35 1 0 0 1 1

16:30 16:35 11.14769 35.523047 42 4 0 0 1 1

16:35 16:40 12.80235 30.931814 45 1 0 0 1 1

16:40 16:45 11.696 33.857729 43 2 0 0 1 1

16:45 16:50 12.99294 30.478088 45 2 0 0 1 1

16:50 16:55 13.515 29.300777 47 3 0 0 1 1

16:55 17:00 8.972 44.137316 43 0 0 0 1 1

17:00 17:05 12.11 32.700248 36 3 0 0 1 0

17:05 17:10 11.065 35.788522 33 2 0 0 1 1

17:10 17:15 12.776 30.995617 40 1 1 0 1 1

17:15 17:20 12.58667 31.461864 52 0 1 0 1 1

17:20 17:25 9.38 42.217484 39 0 0 0 1 1

17:25 17:30 13.208 29.981829 55 1 0 0 1 0

iiiimpact of parking maneuvers on mpact of parking maneuvers on

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