development of vehicle lane changing model...
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DEVELOPMENT OF VEHICLE LANE CHANGING MODEL
WITHIN U-TURN FACILITIES
MOHD SHAFIE BIN NEMMANG
A thesis submitted in
fulfillment of the requirement for the award of the
Master’s Degree in Civil Engineering
TITLE
Faculty of Civil and Environmental Engineering
Universiti Tun Hussein Onn Malaysia
AUGUST 2017
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ACKNOWLEDGEMENT
In the name of Allah, to the Most Gracious and Most Merciful,
I wanted to express greatest gratitude and sincere appreciation to my main
supervisor, Ir. Dr. Raha Binti Abd Rahman and my co-supervisor, Dr Jezan Bin Md.
Diah for their guides and knowledge with their kindness and patience that they have
shared to me in the past two years of research. They have shown me the importance of
knowledge by doing this research and motivation to achieved success to finish this
thesis. This appreciation also for grant U378 under Ir. Dr. Raha Binti Abd Rahman for
all the support in finishing all the test and for the conferences and publication. Thank
also to the Ministry of Education for sponsoring my study with MyBrain15.
I want to thank my parents Nemmang Bin Laming and Rohani Binti Tahang
for all the support. This thank also for Assoc. Prof. Dr. Afandi Bin Ahmad and Mr.
Muhammad Muzakkir Bin Mohd Nadzri who gives support, encouragement and
sacrifice for my journey in finishing my master’s research.
An acknowledgement for all the people and friends that helped and supported
me to finish my thesis from the beginning until the end of this research especially from
Perwira Residential College staff and housemate.
Finally, an appreciation is also extended to all academic and non-academic
members of the Faculty of Civil and Environmental Engineering, Universiti Tun
Hussein Onn Malaysia (UTHM) and Transportation Laboratory, Universiti Teknologi
Mara (UiTM) for assisting me in my works.
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Special for
Beloved Parents
Nemmang Bin Laming & Rohani Binti Tahang
Siblings
Mohd Shahrin Bin Nemmang, Mohd Safri Bin Nemmang, Nurfaziela Binti
Nemmang, Nur Syazwani Binti Nemmang and Nur Syahirah Afiqah Binti Nemmang
Dedicated Supervisor
Ir. Dr. Raha Binti Abd Rahman
Dedicated Co-supervisor
Dr. Jezan Bin Md. Diah
Supporting friends
Muhammad Muzakkir Bin Mohd Nadzri, Muhammad Faris Bin Roslan, Muhammad
Asif Bin Suhaimi, Mohd Naqiuddin Bin Zamri, Khairul Anuar Bin Raman, Wan
Muhammad Fadhli, Mohd Arief Azraei, Kasbi Bin Basri, Khairi Bin Supar
And
Perwira Residential College, Universiti Tun Hussein Onn Malaysia
Thank you for all your supports and always being there for me.
DEDICATION
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ABSTRACT
U-turning movements at roadway segments are channelized and aided with splitting
islands in the middle so that drivers can be on their desired trajectories and the road
segment are often accompanied with weaving, merging and diverging movement. The
objectives are to determine the speed differences (Vd), reaction time (RT) and safe
distance where from those parameters and their relationships, the statistical model was
developed and used in estimating the safe distances to execute the lane changing within
the U-turn facilities. The data were taken from the field which is video recording,
picture and geometric design and was used to simulate the driving simulator like an
actual environment while the driving simulator come out with the speed differences,
reaction time and distance as a raw data. Result show that the mean value of reaction
time (RT) of the driver in making the lane changing at the U-turn area is 2.5s where
the distance to execute the lane changing is 16.467m from the merging vehicle.
Through the relationship between the RT, Vd and distance of the subject vehicle within
U-turn facilities, the findings from the statistical model has been developed with the
equation [Safe distance = (13.448 + 1.410 RT – 0.075 Vd)]. The model was used to
estimate the safe distance which is 15.00m within U-turn facilities. The result shown
that the safe distance will increase when the driver slowing down their speed and
increasing the reaction time. The research concluded that the speed difference and
reaction time have a significant relationship in estimating the safe distance in executing
the lane changing within U-turn facilities.
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ABSTRAK
Pergerakan pusingan U pada segmen jalan biasanya dibantu oleh pemisah pada
bahagian tengah jalan yang membolehkan pemandu berada pada kawasan yang betul
dan kebiasaanya disertai oleh pergerakan seperti weaving, merging dan diverging.
Objektif kajian ini adalah untuk mendapatkan perbezaan halaju, masa tindak balas dan
jarak selamat dalam melakukan pertukaran lorong dimana melalui parameter-
parameter tersebut dan hubungkait, model pertukaran lorong dibangunkan dan
digunakan untuk membuat anggaran jarak selamat untuk melakukan pertukaran lorong
dalam kawasan pusingan U. Data diambil pada kawasan kajian seperti rakaman video,
gambar dan rekabentuk jalan dimana ianya digunakan untuk mensimulasikan driving
simulator menyamai persekitaran jalan sebenar dan driving simulator akan
mengeluarkan data-data yang diperlukan seperti halaju, masa tindakbalas dan jarak
melakukan pertukaran lorong. Hasil kajian ini menunjukkan purata bacaan untuk masa
tindakbalas pemandu dalam melakukan pertukaran lorong iaitu 2.5s dimana jarak
daripada merging vehicle ialah 16.467m. Melalui hubungkait diantara masa
tindakbalas (RT), perbezaan halaju (Vd) dan jarak kenderaan subjek kepada merging
vehicle, model statistik telah dibina iaitu [Jarak selamat = (13.448 + 1.410 RT – 0.075
Vd)]. Model tersebut telah menganggarkan jarak selamat untuk melakukan pertukaran
lorong iaitu 15.00m daripada merging vehicle. Kajian ini juga menunjukkan jarak
selamat akan meningkat apabila pemandu menurunkan halaju dan meningkatkan masa
tindakbalas mereka. Kajian ini membuat kesimpulan bahawa perbezaan halaju, masa
tindakbalas mempunyai hubungkait yang ketara dan penting dalam membuat anggaran
jarak selamat untuk melakukan pertukaran lorong dalam kawasan pusingan U.
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CONTENTS
TITLE i
DECLARATION ii
ACKNOWLEDGEMENT iii
DEDICATION iv
ABSTRACT v
ABSTRAK vi
CONTENTS vii
LIST OF TABLES xii
LIST OF FIGURES xiii
LIST OF SYMBOLS AND ABBREVIATIONS xv
LIST OF PUBLICATIONS xvii
LIST OF APPENDICES xviii
CHAPTER 1 INTRODUCTION 1
1.1 Overview 1
1.2 Problem statement 2
1.3 Research Objectives 5
1.4 Research scope and limitations 5
1.5 Significance of research 6
1.6 Thesis layout 6
CHAPTER 2 LITERATURE REVIEW 8
2.1 Introduction 8
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2.2 U-turn road segment 9
2.3 Midblock U-turning facilities 10
2.4 Types of vehicle U-turn in Malaysia 13
2.4.1 Light vehicle U-turn 14
2.4.2 U-turn for all vehicles 14
2.5 Types of lane changing in traffic movement 15
2.5.1 Understanding of weaving process in traffic
movement 16
2.5.2 Understanding of merging process in traffic
movement 17
2.5.3 Understanding of diverging process in traffic
movement 18
2.6 Previous method in gathering the data 19
2.6.1 Studies using the video recording 19
2.6.2 Studies using driving simulation 20
2.7 Drivers lane selection and lane changing behaviour 22
2.7.1 Previous study of lane changing 23
2.7.2 Decision making to execute lane changing 25
2.8 Space mean speed and speed differences 26
2.8.1 Previous studies for speed 27
2.8.2 Effects of speeding over speed limit 29
2.9 Previous studies for driver reaction 29
2.10 Factors affecting driver reactions 31
2.10.1 Human factors 32
2.10.2 Environment factors 33
2.10.3 Mechanical factors 34
2.10.4 Road layout and design factors 35
2.11 Safe distance in execute the lane changing 36
2.12 Chapter summary 37
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CHAPTER 3 METHODOLOGY 39
3.1 Introduction 39
3.2 Research methodology framework 39
3.3 Research data collection 41
3.3.1 Video recording and picture capture 41
3.3.2 Site road measurement 42
3.4 Research location and criteria of site selection 42
3.5 Screen development for driving simulator 44
3.6 Number of sample and driver criteria 46
3.7 Driving simulation procedure for data collection 48
3.8 Procedure in analyze the driver speed 49
3.9 Statistical analysis for driver speed 50
3.10 Procedure in analyze driver reaction time 51
3.11 Procedure in determine driver speed differences in
making lane changing 52
3.12 Procedure in determine driver distance in making lane
changing 53
3.13 Procedure in develop the statistical model for lane
changing 54
3.13.1 Determine the Data Outliers 55
3.13.2 Descriptive Statistic 57
3.13.3 Determine the dependent and independent
variable 57
3.13.4 Multicollinearity test for the variables 57
3.13.5 Multiple regression of the data 58
3.13.6 Validity check of the result 58
3.13.7 Model validation 59
3.14 Predicted safe distance by using the model 59
3.15 Chapter summary 60
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CHAPTER 4 RESULT AND ANALYSIS 61
4.1 Introduction 61
4.2 Road profile in determining the driver speed 62
4.3 Driver speed 64
4.3.1 Significance test of driver speed 65
4.3.2 Driver speeding discussion 66
4.4 Reaction time of driver 67
4.5 Relationship between speed with reaction time of
driver 69
4.6 Distances of the driver in execute the lane changing 70
4.7 Determining driver speed differences 71
4.8 Determining the driver distances with merging vehicle
at U-turn area 72
4.9 Data boxplot and outliers 72
4.9.1 Boxplot of distance data 72
4.9.2 Boxplot of speed data 73
4.9.3 Boxplot of reaction time data 74
4.10 Descriptive analysis of speed, reaction time and
distance 75
4.10.1 Descriptive and frequencies analysis for distances
data 75
4.10.2 Descriptive and frequencies analysis for speed
data 76
4.10.3 Descriptive and frequencies analysis for reaction
time data 77
4.10.4 Multicollinearity result for the variables 78
4.10.5 Developing the lane changing model 78
4.10.6 Regression analysis for final model in estimating
safe distance 80
4.10.7 Model validation 84
4.11 Estimation of safe distance 86
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4.12 Chapter summary 88
CHAPTER 5 CONCLUSION 90
5.1 General 90
5.2 Conclusion 90
5.3 Knowledge contribution 92
5.4 Suggestions to solve the U-turn affair 92
5.5 Recommendations for future research 93
REFERENCES 94
APPENDIX 105
xii
LIST OF TABLES
1.1 Summary of U-turn from KM1 to KM21 FT050 Batu Pahat -
Kluang 4
2.1 Type of vehicles definition 13
3.1 Drivers’ characteristics 47
4.1 Descriptive statistic for speed during U-turn area and speed
before approaching U-turn area 66
4.2 T-test result for speed during and speed before approaching
midblock U-turn area 66
4.3 T-test result for speed during U-turn area and speed limit
approaching U-turn area 66
4.4 Descriptive statistic for relationship between speed during U-
turn area and reaction time of the driver 69
4.5 Pearson correlation result for relationship between speed during
U-turn area and reaction time 69
4.6 Descriptive statistic of the distances data 75
4.7 Descriptive statistic of the speed data 76
4.8 Descriptive statistic of the reaction time data 77
4.9 Multicollinearity test of the variables 78
4.10 Safe distance model development coefficients 78
4.11 Analysis of variance (ANOVA) for final model safe distance 81
4.12 Regression analysis for final model for estimating safe distance 82
4.13 Distance comparison for model and validation 85
4.14 Speed comparison for model and validation 85
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LIST OF FIGURES
1.1 Malaysia Accident Data (Royal Malaysia Police, 2014) 3
1.2 Johor Accident Data (Royal Malaysia Police, 2014) 4
2.1 Example of U-turn Road Segment 10
2.2 Example of Median U-turn (Rahman and Ben-edigbe, 2015) 12
2.3 Signboard of U-turn for Light Vehicle Only 14
2.4 Signboard of U-turn for All Vehicles 15
2.5 Example of Changing Lane Process (Ben-Edige et al., 2013) 16
2.6 Weaving Process in Road Segment 17
2.7 Merging Process in U-turning Facilities Road Segment 18
2.8 Diverging Process in U-turning Facilities Road Segment 19
2.9 Example of Driving Simulator 22
2.10 The driver’s Action When Facing with Various Queue Lengths
Approaching the U-turn Segment 23
2.11 Flowchart for Decision Making in Approaching U-turn Facilities
(Meng & Weng, 2012) 26
2.12 Safe Distance Visualization for the Driver 37
3.1 Research Methodology Framework 40
3.2 Flowchart of the Driving Simulator Processes Involved 41
3.3 Geometric Layout for U-turn facilities at KM15 road FT050
Batu Pahat - Jalan Kluang 42
3.4 Site Location 1- KM15 Jalan Kluang 44
3.5 Process in Developing the Midblock U-turn in for Screen View
in Driving Simulator 45
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3.6 View for the Driver Inside the Driving Simulator 46
3.7 ASIS Driving Simulator 49
3.8 Area of Driver Mean Speed Taken 50
3.9 Determining the time for V0 and V1 52
3.10 Determining the V0 and V1 in Raw Data 53
3.11 Line Deflection of Steering Position Versus Time 54
3.12 Determine the Coordinate Points 54
3.13 Example of Outliers in the Data 56
3.14 Parameter in Boxplot 56
4.1 Road Profile in Determine the Speed of Driver for During and
Before U-turn Area 63
4.2 Speed Profile of the Driver 64
4.3 Speed Before Against Speed During in Approaching Midblock
U-turn Area 65
4.4 Reaction Time of the Driver 68
4.5 Lane Changing Distance Profile 71
4.6 Determine the Speed Difference 71
4.7 Boxplot for Distances Data 73
4.8 Boxplot for Speed Data 74
4.9 Boxplot for Reaction Time Data 74
4.10 Predicted and Observed Value of the Model 79
4.11 Residual Versus Fitted Values for Safe Distance Model 83
4.12 Kolmogorov – Smirnov Normal Probability Plot of Residual for
Safe Distance Model 83
4.13 Shapiro – Wilk Normal Probability Plot of Residual for Safe
Distance Model 84
4.14 Safe Distance Prediction Vs Observed Distances 87
xv
LIST OF SYMBOLS AND ABBREVIATIONS
FT - Federal Route
KM - Kilometer
m - Meter
m2 - Meter square
RMP - Royal Malaysia Police
JKR - Malaysia Public Work Department
V - Speed
RT - Reaction Time
SSD - Stopping sight distance
D - Distance
Safe distance - Distance in approaching the U-turn
DLC - Discretionary lane changing
MLC - Mandatory lane changing
s - Second
β - Beta
% - Percent
tc - Critical gap
ADT - Annual daily traffic
R5 - Road standard 5
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g - Gravity
SD - Standard deviation
LAA - Lane assignment approach
ANOVA - Analysis of variance
km/h - Kilometer per hour
ASIS - Automotive simulator for driving behaviour and
competency evaluation
VIF - Variance inflation factor
UTHM - Universiti Tun Hussein Onn Malaysia
UiTM - Universiti Teknologi Mara
xvii
LIST OF PUBLICATIONS
Published
An Overview of Vehicles Lane Changing Model Development in Approaching at U-
turn Facility Road Segment. (2016). Jurnal Teknologi (Sciences & Engineering). Vols.
78 (7-2). Pp. 59 – 66.
Analysis of Speeding Behaviour During Approaching the U-turn Facility Road
Segment. (2017). MATEC Web of Conferences. International Symposium on Civil and
Environmental Engineering (ISCEE 2016). Vols. 103.
Comparison of Traffic Speed Before, During and After “Banci Lalu Lintas” at Federal
Road FT005. (2017). MATEC Web of Conferences. International Symposium on Civil
and Environmental Engineering (ISCEE 2016). Vols. 103.
Accepted
Development of vehicle lane changing model in approaching the U-turn facility road
segment. (2017). Journal of Traffic and Transportation Engineering (JTTE).
In review
Development simulation program by using unity3d software to determine the
aggressiveness of driver. (2017). Journal of Traffic and Transportation Engineering
(JTTE).
xviii
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Example of data output from driving simulation 106
B Example of data analysis in SPSS 111
C List of publication 113
D Driving simulator promotion 116
E Simulator driving form 117
F Driving simulator screen development 118
G Data tables 120
H Calculations 124
I Procedure for the driver and driving simulator technician 125
1.1 Overview
Roads serve as the primary mean of access to employment, services and social
activities. Moreover, by linking people and other modes of transport, roadways are a
tremendous asset for achieving greater travel passage within and beyond Malaysia.
Generally, roads are built to provide better accessibility and enhance mobility in
Malaysia. The capital city is Kuala Lumpur. Statistic department (2010) described that
Malaysia consists of thirteen states and three federal territories and has a total landmass
of 329,847m2 separated by the South China Sea into two similarly sized regions,
Peninsular Malaysia and Malaysian Borneo. The researcher also stated that in year
2010, the population was exceeded from 27.5 million and now in 2015 it has grown
into 30 million, with over 20 million living on the peninsular. Malaysia has a good
road network which are paved or unpaved, private or public. Public roads are often
referred to as highways and a road network is a combination of highways. A highway
irrespective of functional classification is made up of segments and
intersections/interchanges.
In Malaysia, peak hour traffic conflicts and congestions have continued to
worsen at the highway intersections. One commendable attempt by authorities to solve
the problems of intersection conflicts and congestions problems is through the
installation of direct midblock facilities that will allow motorists to make U-turning
movements before reaching the intersection (Ben-Edigbe et al., 2013). U-turning
movements involve the diverging, weaving and merging movement for the vehicle to
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enter the preferred lane. Therefore, the scenario will produce a lot of movement for the
road that may lead to traffic accident and fatal.
A lot of studies have been done about the lane changing behaviour whether for
roundabout, traffic light, junction and road curve/ design for heavy and light vehicle.
The latest research done by Sharma et al. (2017) studied the critical gap for the
merging vehicle to enter the freeways for U-turning operation for median opening and
the studies was focuses on the U-turn without the midblock facility for the vehicle to
accelerate to merge into the freeways. Other than that, Mohanty et al. (2017) studied
the effect the movement of approaching vehicles at the freeways leading them to either
slow down or change their lanes just to avoid the conflict with U-turn and the present
study applies Markov’s process to estimate lane changing patterns of approaching
through vehicles due to the presence of U-turns. However, in this research focusing on
lane changing model. This research was developed a vehicle lane changing model
within U-turn facility road segment. It requires the performance of vehicle movement
by considering the parameters which related to Malaysian driver’s reaction such as
reaction time, speed and distance in executing the lane changing. The establishment of
suitable U-turn and appropriate condition is needed in order to achieve the entire
objectives. The reasonable method to perform the data is by using driving simulator
where this technique can allow data collection to be reliable and well-organized.
Leitão et al. (1999) stated that driving simulator can give a real scenario for the driver
that can make the model valid to use. In addition, this research concerns on the U-turn
road segment situation due to the study implementation. Therefore, this research
provided knowledge, understandings and new findings in the field of traffic
engineering.
1.2 Problem statement
Abdul Manan and Várhelyi (2012) stated that roads accident has caused a
major problem in all over the world especially in Malaysia. Some accidents occurred
because of the aggressiveness and inappropriate driving behaviours of driver. (Abdul
Manan and Várhelyi, 2012; Abdul Manan, 2015a) shows that in Malaysia, there are
56,513 people killed, 234,959 people were slightly injured and 55,295 people were
seriously injured on the road crashes that recorded from year 2000 to 2009.
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Referring to Figure 1.1, Royal Malaysia Police, RMP (2014) shows that Johor
was the second highest for the accident statistic with 64,473 accident cases which is
73,336 number of accident differences behind Selangor with 137,809 cases.
Comparing the number of fatal for that single year, Johor was also the second highest
for the number of fatal with 1,018 with only 50 number of fatal differences compared
to Selangor with 1,068. It shows that Johor has a highest percent of fatal by comparing
the number of accident and the number of fatal statistics.
Figure 1.1: Malaysia Accident Data (Royal Malaysia Police, 2014)
Figure 1.2 shows the accident data for Johor district in year 2014. The figure
shows that Batu Pahat was the second highest in number of accident occurred with
7,445 cases behind the combination of Johor Bahru Utara and Johor Bahru Selatan
area with 22,563 cases. However, for the number of fatal cases, Batu pahat district was
the highest with 165 cases. RMP (2014) showed the number of accident in U-turning
facilities segment in 2014 the number of accident in the U-turn road segment is 469
accident cases with 104 fatal. This cases shows high number of accident occurred in
U-turn that need to re-visualize and rearrange to give a better focus in this types of
facilities. In single year of 2014 that recorded by Batu Pahat police station have stated
1286 accident cases in this FT050 road which is the site location. Table 1.1 shows the
number of U-turn for all types of U-turn where there are 6 midblock U-turn from 8 U-
turn facilities along the KM0 to KM21. Therefore, there are highest possibilities for
the number of accident to keep increasing every year if the area is not treated properly.
4
The Star newspaper reported on 4 May 2009 that Jalan Batu Pahat – Kluang (FT050)
has been identified as the “deadliest stretch of road” in Malaysia, as announced by the
Works Ministry of Malaysia (KKR). That is proved the dangerousness of this road that
can cause a lot of fatal.
Figure 1.2: Johor Accident Data (Royal Malaysia Police, 2014)
Table 1.1: Summary of U-turn from KM1 to KM21 FT050 Batu Pahat - Kluang
Segment Intersection
(km)
U-Turn
All Vehicle Light Vehicle
1 1 – 5 2 1
2 5 – 10 1
3 10 – 15 1 2
4 15 – 18 1
Attitude of the drivers such as anger, selfish and so on contribute to an accident.
For certain drivers, rude manners were the driver attitude that have some difficulties
to change. When the drivers want to change their lane, make a U-turn, at the traffic
light, and enter the roundabout, drivers always consisting impatient, compete and
always in hurry (Md Diah et al., 2008). They would in highly of speed and the
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possibility for an accident to occur with the surrounding vehicles also high when the
drivers are covered by this emotions and manner. Furthermore, (Crundall et al., 2008;
Darren et al., 2009) stated that driver’s reaction can be influenced indirectly in making
all appropriate visual checks and their reactions can be influenced by environment,
mechanical or design of the road. Besides that, Yeoh et al. (2011) stated that attitude
changes is believed to increase the driving reaction to achieve the traffic safety.
Through this research, the model has been developed based on the safe distance, speed
and reaction time using the simulator and the model predicted the safe distance for
lane changing within the U-turn facilities.
1.3 Research Objectives
The objectives of the research are:
i. to determine driver’s speed, reaction time and distance in approaching U-turn
facilities;
ii. to developed the vehicle lane changing model within U-turn facilities; and
iii. to predict the safe distance by using the vehicle lane changing model within
the midblock U-turn facilities.
1.4 Research scope and limitations
This research was conducted using driving simulation and visualized based on the
environment of the research area. This research has been done only at multi-lane dual
carriage highways because of direct U-turning movements with the midblock can be
found only at this types of road. Central medians are the medium to separate the
carriageways where it can be found at the federal routes. Other than that, this research
was carried out in a dry condition and daytime to make sure the data taken is valid
without any disruption for vehicle to slow down at the site area. Therefore, the data
taken is corresponding to the driver reaction when passing through the site area.
Driving simulator are used to simulate the condition and environment of the road
where driving simulator is fully used in this research in providing the data needed. This
research focus on determining the speed, reaction time and distance from subject
vehicle to the merging vehicle due to changing lane at U-turn facility road segment
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and reaction of the driver when passing through the midblock U-turn facilities at
CH000 – CH021 of FT050 Jalan Batu Pahat – Kluang.
1.5 Significance of research
This research finds out the speed of the driver inside the freeways of the U-turn area
which are comparing with the speed limit at 60km/h. At the freeways, the speed limit
is 90km/h while at the U-turn area is 60km/h. Therefore, along the FT050 Jalan Batu
Pahat – Kluang has a lot of speed limit changing for the driver to adapt. Therefore, this
research finds out that driver tend to speeding based on their normal speed which is
about 90km/h based on the usual speed limit. The lane changing model predicted the
safe distance within U-turn facilities. Other than that, this research also developed a
model within the U-turn facilities that can be used for driver to implement in real
scenario. Therefore, this research will provide the understanding and guide for the
driver in order to reduce the number of accident especially in U-turn facility road
segment. This research also directly knows the reaction of drivers whether they have
a precaution on a safety issue with slowing down the speed or given a chance for
merging vehicle at the U-turn road segment to enter the main road of the highways.
1.6 Thesis layout
This section provides brief information about each chapter and there are six chapter in
total for this thesis. Chapter one elaborated the introduction and explanation to the
problem, aims, and contribution of the research. It is giving general explanation and
introduction on the research and the scope of work. Other than that, Chapter two takes
a literature review or theoretical review which take a closer look at the U-turn, driver
reactions and the driving simulator. Additionally, literatures on multilane highways
and midblock facilities are also presented to give an understanding about the work
involved in this research.
Chapter three explained the research methodology on midblock U-turn
facilities data collection. It gives the criteria for site selection, the survey method and
the driving simulator used to carried out all the test and analysis. Chapter four Since
this research investigate the effects of midblock facilities road segment into the driver
reactions on speed, reaction time, and distance to execute the lane changing, this
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chapter explained the results of that parameters especially in approaching a U-turn
facility road segment. Other than that, Chapter four also explained about the process
and analysis involved in developing the statistical model. The model using three
parameters which are, distance, speed differences and the reaction time of the driver.
All the data has been screened and follow all the process involved in developing the
statistical model using SPSS. Other than that, this chapter involved the regression
analysis of the model and the model validation in order to ensure the model developed
are relevant and valid to be used. Chapter five shows the summary and conclusion in
achieving the aim of the research. The research aim has been achieved by completing
the three objectives that has been concluded in this chapter.
2.1 Introduction
The change from one lane to next adjacent lane of a vehicle is defined as a lane change.
It is involving the decision making such as in a midblock facilities road segment that
always been done for any driver whether they are notice it or not. This is because,
driver’s will experience different type of situation in approaching any midblock U-
turn facilities because of the different desire and target of driver’s that contribute to
various type of movement such as merging, diverging and weaving. Correspondingly,
Rahman and Hassan (2014) asserted that a U-turn refers to vehicles performing a 180°
rotation to reverse the direction of travel. Often U-turning movements at roadway
segments are channelized and aided with splitting islands in the middle so that drivers
can be on their desired trajectories. Therefore, this chapter discusses and explained
various facts and previous studies with references to the topic and objectives of the
research which is the development of vehicle lane changing model within U-turn
facilities which means to find out the speed, reaction time and distances that can guide
the driver’s when approaching the U-turn facilities road segment by develop the
model. This chapter given a better understanding about the types and condition of
highways in Malaysia especially in U-turn and also the guidelines from the previous
study to complete the test involved. Other than that, driver speeds, reaction time are
discussed since this research involves the speed and reaction time in collecting the data
and also developing the model.
In addition, which speed drivers choose to drive in various road and traffic
conditions is an important characteristic towards their reaction. The most influential in
9
determining driver’s choice of speed is the characteristics of drivers. Taylor (2011)
stated that characteristics of the road they were driving was strongly contributing to
the driver speed. Furthermore, Summala (2000) mentioned that to allow an assessment
to be made of whether driver response times depend on vehicle speed which is in
accident litigation, the legal process often tends to determine whether the participant
driver reacted to the impending collision within “acceptable” time, vehicle speed was
observed for some circumstances. Correspondingly to Benedetto et al. (2011), to avoid
the collision the drivers have to react to the sudden braking of the lead vehicle. While
Rahman and Ben-edigbe (2015) showed that travel speed at free-flow section
decreased significantly at the median opening zone at all sites. Speed reductions are
greater at the diverging sections than the merging sections. At the merging section
vehicles exiting from the U-turn facilities must give a way to all approaching vehicle,
hence the slight drop of driver speed is about 5 percent at the merging sections.
Nonetheless the assertion that median openings would cause travel speed reduction
remain valid. Therefore, the reaction of driver’s in approaching the midblock U-turn
facilities need to be study properly.
2.2 U-turn road segment
Often U-turn traffic movements at roadway segments are channelized and aided with
splitting island so that the drivers can be on their desired trajectories. Figure 2.1 show
the example of U-turn that had been used nowadays in many countries. Midblock U-
turn facilities are often constructed as a cost effective way of alleviating congestion
and road safety problems. Some highway midblock U-turn facilities are built to
complement existing road geometric design; others are built as a complete replacement
to existing facilities on the premises that they will reduce conflicts and ease congestion
at adjoining intersections. That may be so, but there are road safety consequences that
are often ignored. It is inherent because of driver’s desire to position their vehicle along
the appropriate carriageway lane. Misjudgment of ensuing gaps is not an option. When
exiting the facility, driver may reject gap on the major road and wait for a subsequent
gap. Poor gap acceptance decisions have severe consequences. They may cause traffic
shockwave and lead to accidents. In any case, the existence of traffic shockwave at the
weaving area of midblock u-turning facilities is a clear indication of inherent road
safety risk.
10
Figure 2.1: Example of U-turn Road Segment
Ben-Edigbe et al. (2013) studies about the effect of U-turning manoeuvres at
midblock facilities on traffic kinematic waves aimed to determine the U-turning
manoeuvres at roadway midblock facilities inducing the extent of traffic kinematic
waves, they have surveyed 150,000 vehicles where the data supplemented with
information culled from the Malaysian Public Work Departments highway design
manual and find out that midblock facilities can be called to account for severe
kinematic waves on approached to the entry lane is has no evidence to suggested.
When merging, significant kinematic waves of about 21 km/h occurred. The research
concludes in the results showed acceleration and merging from exit lane to major
carriageway caused more kinematic waves rather than deceleration and diverging to
entry lane. Therefore, this research was given a very good contribution to help in
reducing the number of accidents and give a good come out in order to make a better
condition in U-turn facilities.
2.3 Midblock U-turning facilities
Midblock facilities in Malaysia are built as complimentary facilities to existing
infrastructure design, others are built as a complete replacement to existing facilities
on the premises that they will reduce conflicts and ease congestion at adjoining
intersections. U-turning facilities aimed at easing traffic conflicts and pressures at
highway intersections. While some are built as complimentary facilities to existing
11
road geometric designs, others are built as a complete replacement to existing facilities
on the premises that they will reduce conflicts and ease traffic congestions at adjoining
intersections. In Malaysia, where the right hand driving rule is in place, drivers
decelerate when diverging, accelerate when converging at the midblock facilities.
Therefore, it is not surprising that the issue of midblock U-turning facilities has
provoked fierce national debates. Proponents of midblock facilities argued that their
installation has brought some help to motorists plagued with conflicts and congestions
at adjoining intersections. Median U-turn exist at the multilane highways which has
two lanes in each directions and separated by median in the middle of the highways.
Midblock facilities are installed to facilitate vehicle U-turning movement. Divided
highways are separated with rigid barrier or flexible barrier such as landscape with
maximum speed limit of 90 km/h. These multilane highways have many signalized or
non-signalized intersections that the fact is when more intersections in the highways
exist, the more conflict occur. Prasetijo et al. (2014) stated that traffic conflicts are
created when two or more vehicular movements on the roads cross each other and that
conflicts may cause traffic congestion and delay with the possibility of road accidents.
Traffic movement is interrupted by these midblock U-turn junctions. The U-
turn vehicles will be waiting for the large enough gap and make U-turn movement
after arriving in the midblock median opening. Therefore, requirement needed for a
satisfactory design of a U-turn such as width of a highway and median size that must
be sufficient to permit the vehicle to turn without any unconvinced. It must allow the
vehicle moving smoothly to enter the U-turn lane, make a U-turn and merge into the
flow or preferred lane of the road. Figure 2.2 show the example of median U-turn.
Driver can choose any lane whether to choose fast lane or the slow lane before the U-
turn signboard. Whenever any merging vehicle in front enter the fast lane, the driver
on the freeways have to react.
12
Figure 2.2: Example of Median U-turn (Rahman and Ben-edigbe, 2015)
The geometric design of a median U-turn (MUT) intersection introduces some
unique design elements not typically present at a conventional intersection. These
elements include:
i. To facilitate the movements of median U-turn, wide median is often needed.
Typically, the intersection and main crossing street is uniform, but to reducing
the length of the wide median or locate the median on the minor street needed
some variations in the design.
ii. Large enough vehicle path at the U-turn crossover needed to accommodate
trucks and allow for efficient movements through the U-turn by passenger
vehicles.
iii. Design element that providing positive guidance using design elements and
signage needed to reduce chances of driver error and discourage prohibited
turns.
iv. For the U-turn movements that would otherwise be unexpected or not familiar
to driver or motorists needed a signing, marking, and geometric design in order
to promoting safe and efficient movements.
v. In order to promote safe and efficient access to these properties needed a
corridor-wide access strategies and management considerations to properties
along the median street.
The purpose of using midblock facilities is to assist and guide the driver with
right turning movements on multilane highways. Iskandar Regional Development
13
(2011) suggested that one potential treatment to combat congestions and safety
problems at intersections was by the installation of non-traversable medians and
directional median opening. While (Ben-Edigbe et al, 2013; Ben-Edige et al, 2013)
showed that travel speed at free-flow section decreased significantly at the median
opening zone at all sites and there is correlation between traffic safety and midblock
U-turning facilities. Speed reductions are greater at the diverging sections than the
merging sections. At the merging section vehicles exiting from the U-turn facilities
must give way to all approaching vehicle, hence the slight drop of about 5 percent at
the merging sections. Nonetheless the assertion that median openings would cause
travel speed reduction remain valid.
2.4 Types of vehicle U-turn in Malaysia
U-turn in Malaysia can be categorized into two types of vehicle which is for light
vehicle and for all types of vehicle. Abdelwahab and Abdel-Aty (2004) categorized
vehicles into two which are large and small vehicles where the large vehicles typically
are heavy while small ones are light. Light vehicle can be divided into two which is
passenger cars and light commercial vehicles. Heavy vehicle also can be divided into
two which is heavy trucks and buses or coaches. The vehicle type and definitions is
explained in Table 2.1
Table 2.1: Type of vehicles definition
Type of vehicle Vehicle Definitions
Light vehicles
Passenger cars
- Motor vehicles with at least four wheels.
- Used for the transport of passengers.
- Comprising no more than eight seats in addition to the
driver’s seat.
Light
commercial
vehicles
- Motor vehicles with at least four wheels.
- Used for the carriage of goods.
-Weight are between 3.5 to 7 tonnes.
14
Table 2.1: Type of vehicles definition (continued)
Type of vehicle Vehicle Definitions
Heavy vehicles
Heavy trucks - Vehicles intended for the carriage of goods
- Weight are over seven tonnes.
Buses and
coaches
- Used for the transport of passengers.
- Comprising more than eight seats in addition to the
driver’s seat.
- Weight over 7 tonnes.
2.4.1 Light vehicle U-turn
U-turn for light vehicle only means that only light vehicle can make a U-turning
movement and prohibited for heavy vehicles. This type of U-turn exists caused by a
small U-turn facility that make the heavy vehicle difficult to accelerate. Other than
that, this type of U-turn also exists in a signalized junction. However, the function of
U-turn is still the same. The signboard of U-turn for light vehicle are shown in
Figure 2.3.
Figure 2.3: Signboard of U-turn for Light Vehicle Only
2.4.2 U-turn for all vehicles
This type of U-turn is available for all types of vehicle in order to reverse the direction
of travel. Signboard of U-turn for all type of vehicles are shown in Figure 2.4. This
type of U-turn need a big area and U-turn facility that allow the vehicle to accelerate
to enter the fast lane. This signboard can be seen in display about 150m to 200m of
SSD. Therefore, this research was conducted in this type of U-turn because of the
15
availability of diverge and merge vehicle. This signboard was visualized inside the
driving simulation with the same standard as provided by JKR.
Figure 2.4: Signboard of U-turn for All Vehicles
2.5 Types of lane changing in traffic movement
Lane changing is a process of determining the best lane to drive without any
interruption on the road. Lane changing also occurred when a driver wants to enter the
preferred lane of destination. Figure 2.5 shows the example of changing lane process
that usually occurred due to driver lane desire. The process of changing lane will have
caused a weaving, merging and diverging process that will vary the road environment.
Weaving, merging and diverging are traffic stream deft manoeuvre that are often laden
with profound risk of accident occurring. According to Xiaorui and Hongxu (2013),
drivers misjudgment or driving skills of different levels during the lane changing is
likely to cause traffic accidents, and accidents caused by lane changing accounting for
6 percent of the total traffic accidents and causing in delay time in traffic more than 10
percent of total delay time by traffic accidents.
16
Figure 2.5: Example of Changing Lane Process (Ben-Edige et al., 2013)
2.5.1 Understanding of weaving process in traffic movement
Road movement always involving many types of behaviour that make the road had a
various types of uncertainty. One of the movement is weaving. (Ben-Edige et al., 2013;
Kusuma et al., 2014; Rahman and Hassan, 2014) defined weaving occurs when
vehicles crisscross the carriageway lanes often with a view to repositioning for traffic
stream advantage which are crossing two or more traffic stream travelling in the same
direction along a significant length of the roadway without traffic control device. It is
inherent because of driver’s desire to position their vehicle along the appropriate
carriageway lane. When exiting the facility, driver may reject gap on the major road
and wait for a subsequent gap. Poor gap acceptance decisions have severe
consequences. It can cause traffic shockwave and lead to accidents. Weaving is often
triggered on approach to highway ramps, intersections as well as midblock facilities
among others.
Hwang and Park (2005) defined the weaving movement as a situation where
there are two or more traffic streams crossing each other without any aid of traffic
control devices in order to adjust their lane position due to their destination lane or to
seek a chance to pass a vehicle in front of them, each driver therefore seeks the gap
event both in their current lane and target lane. High number of weaving movements
and an aggressive driving movement can create traffic instability and shockwave
effects. In terms of road safety issues, the movement contributes to a high accident risk
considering that the traffic has to share the space at the same time without any
assistance of traffic control. Kusuma et al. (2014) found that in the first 50m to 100m
from the merge point, 25.25 percent of the weaving movements took place and one
17
lane changing movement from 30 percent of the total traffic involved. Figure 2.6 show
the weaving process in road segment. These dangerous manoeuvres beg the question;
‘what are the driver behaviour when approaching the U-turning facilities? What are
the speed of the vehicle whether accelerate or decelerate when see any car want to
merge into the main lane? Wei and Wanjing (2013) showed the weaving section
capacity as the total number of vehicles passing through the section and crossing
among the traffic along the weaving section length. They stated the maximum flow for
through traffic is 2200 PCU/hr/ln and for cross traffic is between 1100 and 1200 per
hour per 76 m of weaving section.
Figure 2.6: Weaving Process in Road Segment
2.5.2 Understanding of merging process in traffic movement
Acceleration and merging is a deft manoeuvre because through traffic flows have
priority in the conflict sections, and vehicle attempting to enter the main lane after
making a U-turn can only do so during larger gaps of successive vehicles in the fast
lane. Major source of conflicts and congestion on motorways were when motorway
merging. Wang (2006) stated that traditional studies of merging behaviour are based
on gap acceptance models developed mainly for urban intersections, which tend to
oversimplify the very complex dynamic interactive merging behaviour.
Merging is more difficult than diverging because through traffic flows are
traversing along the faster lane. It is a very dangerous manoeuvre that can trigger road
accident. This is because drivers along the main lane or fast lane are forced to either
abandon the overtaking move in order to avoid collision. Ben-Edige et al. (2013)
concluded that there is correlation between traffic safety and midblock U-turning
facilities and got the estimated delay of about 8.55s per vehicle at the exit lane of U-
turn. However, it can be easy when driver have cooperation and tolerance during
18
driving in this area as stated by (Wang et al., 2005). They are study sensitivity analysis
of the model suggests that cooperative lane-changing and yielding have a significant
effect on the proportion of merges that are made easily. Similarly, the higher the
proportion that successfully merge into their first choice of gaps when more alert the
drivers.
Figure 2.7 shows the example of merging process in U-turning facilities road
segment. In order to avoid collision, the vehicle in the main lane need to be careful
with the merging vehicle because the merging vehicle need to find the safe gap
acceptance to enter the main lane. Wang (2006) investigated the driving behaviour in
the merging motorway section using both the traffic surveillance and MIDAS data and
the video observation showed that most of the lane-changing occurs at the first
available gap. In the other words, the main traffic and entry-slip road traffic meet at
the end of the taper marking leads to the lane changing occurrence. The length of the
auxiliary lanes in merging area length affects the merging behaviour. In fact, in order
to seek for larger gap rather than accept the first gap, lane change drivers become
conservative along auxiliary lane. The average speed between the lanes is relatively
similar, where most of the lane changing occurs.
Figure 2.7: Merging Process in U-turning Facilities Road Segment
2.5.3 Understanding of diverging process in traffic movement
Diverging in this case means from the main lane the drivers enter the U-turn due to
their preferred destinations. In Malaysia where the right hand driving used, the driver’s
that trying to enter the midblock U-turn facility need to enter the fast lane in order to
enter the U-turn facilities road segment. Figure 2.8 show the example of diverging
19
process in U-turn. The vehicles need to slow down the acceleration in order to enter
the U-turn lane which is vice-versa with the merging process where the vehicles need
to accelerate.
Figure 2.8: Diverging Process in U-turning Facilities Road Segment
2.6 Previous method in gathering the data
Data collection is to answer relevant questions and evaluate outcomes by the process
of gathering and measuring information on targeted variables in an established
systematic fashion. To capture quality evidence that then translates to allows the
building of a convincing and credible answer to questions that have been posed is the
main goal for all data collection. Data collection is the process to run the test in order
to gained information and data from the site. Therefore, in this research was used
several test to collect data which is video recording and driving simulator.
2.6.1 Studies using the video recording
Abdullah et al. (2014) explained that traffic monitoring is a challenging task on
crowded roads. Traditional traffic monitoring procedures are manual, time consuming,
expensive and involve human efforts. Because of the involvement of human effort and
sometimes provide inaccurate monitoring results make it subjective matter. Due to
limited availability of storage and compute resources in the past, large scale storage
and analysis of video streams were not possible. Recent advances in processing, data
storage and communications have made it possible to store and process huge volumes
20
of video data and develop applications that are neither subjective nor limited in feature
sets. Now, it is possible to implement object detection and tracking, behaviour analysis
of traffic patterns, number plate recognition and automate security and surveillance on
video streams produced by traffic monitoring and surveillance cameras.
The importance of video recording is already explained that it is very familiar
nowadays especially in traffic monitoring because of the helpful work that can store
the video and can reduce the human effort. Several new technologies for video display
devices have since been invented like Semi-Automatic Video Analysis (SAVA) that
widely used in United Kingdom (UK). Kusuma et al. (2014) mentioned that in order
to capture the traffic characteristic and driving behaviour on a specific motorway
network, SAVA data has been used in a wide range. (Benedetto et al., 2011; Jooho et
al., 2006; Kusuma et al., 2014; Liu. R and Wang, 2007) used video recording in
developing model and analysis of driving behaviour and validation of car following
model which are can extract the movement characteristic of each vehicle and the
vehicle trajectory. Corresponding to Itoh et al. (2007), they extracted behaviour of
other vehicles on the passing lane by analyzed the video image stored by the driving
recorder and by observing the video image. Kassner and Vollrath (2006) used the
CAN-bus provide information about velocity, braking, accelerating and the steering
wheel angle during the merging process and CAN-bus equipment can be used to
observed the head of the driver and the surroundings in front and the behind the car.
Wu et al. (2014) also used CAN-bus in studies about the driving behaviour to identify
relevant features extracted from a frontal video camera. Other than that, the cameras
system makes the possible to localize exactly the point driver changing lane where it
can help in setting up the simulation and scenario for driving simulator.
2.6.2 Studies using driving simulation
Experts conducting analysis and reconstruction of traffic accidents now have at their
disposal advanced computerized systems of collision and traffic simulation. Most of
them can create a computer animation of a reconstructed complex crash situation, so
it could be able to present a virtual simulation of an accident. The result of the
reconstruction is thus presented not as complex calculations that has been used, but in
the form of an animated film, in which the background can be a real environment scene
of an accident that actually occurred. This animation is very suggestive for a
21
participant, so it should be performed with the very best and sense of responsibility.
The credibility of the reconstruction carried out by an expert depends on many factors
where one of the most important is the selection of the input parameters for the
calculation. For instance, this applies to the adhesion coefficient, coefficient of
restitution, reaction time as stated by Jurecki and Stańczyk (2014). Driving simulators
are used for entertainment as well as in training of driver's education courses taught in
educational institutions and private businesses and are also used for research purposes
in the area of human factors and medical research, to monitor performance, driver
behaviour, and attention. In the car industry, it is used to design and evaluate new
vehicles or new advanced driver assistance systems.
Hwang and Park (2005) stated that modeling lane changing behaviour is
complex because it considers three elements which is the possibility of changing lanes,
the need to change lanes and the trajectory for changing lanes. It also must be
considered not only the vehicle in the front, but also the vehicle nearby. Many studies
have been done by using driving simulator in order to find the behaviour of the driver’s
Islam and Hadhrami (2012) proposed a mandatory lane changing model and extended
the work by developing a new model for heavily congested traffic flow by using the
driving simulator and find out that there are very little gaps of acceptable lengths in
heavy congestions. Hence, by forcing the lag vehicle to slow down, a forced merging
model is proposed which can capture instances of merging through the creation of gap.
Md Diah, J. et al. (2012) used driving simulator in determining the lane changing
behaviour in approaching the heavy vehicles at signalized junctions where the research
find out that 15.4 percent reaction time and speed contributing to the safe changing
lane distance at the signalized junction. (Ben-Edigbe et al., 2013; Rahman and Hassan,
2014) used the same layout of survey site to study the midblock U-turning impact
where the fast lane is influenced by diverging vehicle that want to enter the U-turn lane
while in the slow lane are influenced by merging vehicle. It shows this layout can
collect the data for speed that can contribute in achieving the objective. Figure 2.9
show the example of driving simulator that is very common used nowadays and it
show like driving in real situation.
22
Figure 2.9: Example of Driving Simulator
2.7 Drivers lane selection and lane changing behaviour
When the drivers are not satisfied with the driving condition in the current lane and
wish to gain a speed advantages, discretionary lane changing (DLC) manouvres are
executed. That means the drivers want to maintain or increase the speed because of the
disruption from any vehicle such as diverge car in U-turn facility. When the driving
conditions are not satisfactory, automatically the drivers will compare the driving
conditions to the other lane. Important factors affecting the decision include the
difference between the density of traffic, the presence of heavy vehicle in the target
lane, speed of traffic, merge and diverge vehicle. Lane changing process is One of the
Malaysian driving behaviour. Because of intolerant driving while selecting and
making lane changing, it creates problems such as delays and accidents. Figure 2.10
illustrated the real scenario of the driver’s action whether to choose lane one, two or
three for the driver from eastbound on lane two and driver’s action whether to choose
lane five or six when the driver drives on lane five from westbound and facing on
various kind of diverging and merging movement at the U-turn road segment. The
drivers will try to make a lane changing when facing this type of situation.
Zhao et al. (2014) found that 640 mandatory lane changing (MLC) occurred from a
naturalistic driving database. The result show that MLC are approximately 4.5 times
to have a critical gap to occur.
Therefore, this research focused on providing a new model of lane changing in
approaching merging vehicle at the U-turn facility road segment that can assist driver
23
to facing various possibilities in U-turn facility especially in making the lane changing
from the merging vehicle.
Figure 2.10: The driver’s Action When Facing with Various Queue Lengths
Approaching the U-turn Segment
2.7.1 Previous study of lane changing
(Md Diah, J. et al., 2010; Md Diah, J. et al., 2011) claimed that there is a “lack of
knowledge” of corresponding methods for the traffic performance at the intersection
and of Malaysian driver behaviour. Many researcher have defined the lane changing
such Zhao et al. (2014) defined lane change as a driving manoeuvre that moves a
vehicle from one lane to another lane where both lanes have the same direction of
travel. Moridpour et al. (2010) asserted that lane changing models need the accuracy
of essential components of microscopic traffic simulation in develop lane changing
model. It is assumed that a driver makes consideration between expected own
advantage and the disadvantages when a lane change is considered.
Kesting et al. (2007) divided the lane changing model into multistep process which is
on a strategic level, the drivers know the route and a network that can influence the
lane choice. In the tactical stage, the intended lane change is prepared and initiated by
advance acceleration. Finally, in operational stage, immediate lane change is both safe
and desirable. Their research in develop car-following model use a concept to simulate
two lane freeway traffic with an on-ramp as merging zone.
24
Other than that, in car following lane changing model in freeways also have
been studied. Xiaorui and Hongxu (2013) studies the car following behaviour using
the model that developed by MATLAB simulation. While, Al-Kaisy and Karjala
(2010) studied the car following interaction in two lane rural highways that is studies
about time headways and speed choice in executed the lane changing.
Guo et al. (2013) studied about lane change using driving simulation on multilane
freeway. The research finds out the characteristics such as speed, acceleration rate, and
glancing of drivers where the speed of the driver in executed the lane changing depends
on the length and space available with the front vehicle. The behaviours of lane
changing also have been studied. Lane changing behaviours has a direct influence on
traffic safety. Tian (2011) claimed that in lane changing process, the different types of
drivers have a different manner and in different environment will show different
behaviour. While Aghabayk (2012) claimed that different longitudinal driving manner
to a large extent determines the distributions of speeds and densities throughout the
lanes can lead to lane changing. With the traffic scale growing, lane changing has
become driving routine that driver facing.
Deeper understanding of long term driving assistance is required to provide
drivers with versatile and various advanced assistant services especially in midblock
U-turn facilities road segment. Some studies on lane changing behaviour have been
done such as by Wei and Wanjing (2013) that studies simulation based study on a lane
assignment approach for freeway weaving section. Their study on freeway simulator
with real world weaving data with new concept which is lane assignment approach
(LAA) to reduce the disruption between the cars of different destinations and prevent
unsafe weaving manouevres. Moreover, Keyvan-Ekbatani et al. (2015) categorize the
lane change decision process on freeways using microscopic traffic simulation. They
asked driver to drive on a freeway with a camera equipped vehicle and asked the driver
to comment on lane and speed preferred. Zheng et al. (2013) studies the effects of lane
changing on the immediate follower by measuring the induced transient behaviour and
the change of driver behaviour because of immediate follower. Lv et al. (2011) studied
lane changing behaviour on three lane highways using simulation. The optimal
velocity model obtained continuous position and velocity in space and time. However,
in this study, it focused on the lane changing in approaching the U-turn facility road
segment because of the diverge vehicle. Previous study about the DLC’s carried out
by Zhao et al. (2014) found that 2035 numbers of DLC’s occur and find out over 10
REFERENCES
Aarts, L., & Van Schagen, I. (2006). Driving speed and the risk of road crashes: A
review. Accident Analysis and Prevention, 38(2), 215–224.
Abdelwahab, H., & Abdel-Aty, M. (2004). Investigating the effect of light truck
vehicle percentages on head-on fatal traffic crashes. Journal of Transportation
Engineering-Asce, 130(August), 429–437.
Abdul Manan, M. M. (2015). Exploring the Use of Road Safety Legal Instruments to
Address Powered Two-Wheeler Safety Policies in Low-Middle Income Countries.
Malaysian Institute of Road Safety Research (MIROS).
Abdul Manan, M. M., & Várhelyi, A. (2012). Motorcycle fatalities in Malaysia. IATSS
Research, 36(1), 30–39.
Abdullah, T., Anjum, A., Tariq, M. F., Baltaci, Y., & Antonopoulos, N. (2014). Traffic
monitoring using video analytics in clouds. 2014 IEEE/ACM 7th International
Conference on Utility and Cloud Computing Traffic.
Aghabayk, K., Sarvi, M., & Young, W. (2012). Effective variables on following
behaviour of heavy vehicle drivers. Australasian Transport Research Forum,
(September), 1–14.
Ahmadi, J., Basiri, E., & MirMostafaee, S. M. T. K. (2016). Optimal random sample
size based on Bayesian prediction of exponential lifetime and application to real
data. Journal of the Korean Statistical Society, 45(2), 221–237.
Ahmed, K. I. (1999). Modeling Drivers ’ Acceleration and Lane Changing Behavior -
PhD Thesis. Massac usetts Institute of Technology, Cambridge, MA.
Alexander, J., Barham, P., & Black, I. (2002). Factors influencing the probability of
an incident at a junction: Results from an interactive driving simulator. Accident
Analysis and Prevention, 34(6), 779–792.
95
Al-Kaisy, A., & Karjala, S. (2010). Car-following interaction and the definition of
free-moving vehicles on two-lane rural highways. Journal of Transportation
Engineering, 136(10), 925–931.
Ambak, K. (2009). Intelligent transport system for motorcycle safety and issues.
European Journal of Scientific Research, 28(4), 600–611.
Bartlett, J. E., Kotrlik, J. W. K. J. W., & Higgins, C. (2001). Organizational research:
Determining appropriate sample size in survey research appropriate sample size
in survey research. Information Technology, Learning, and Performance Journal,
19(1), 43–50.
Baruya, A. (1997). A review of speed-accident relationship for european roads.
Transportation Research Laboratory, Berkshire, UK. VTT Communities &
Infrastructure (VTT, Finland).
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting linear mixed-effects
models using lme4. Journal of Statistical Software, 67(1), 51.
Bella, F., Calvi, A., & D’Amico, F. (2014). Analysis of driver speeds under night
driving conditions using a driving simulator. Journal of Safety Research, 49.
http://doi.org/10.1016/j.jsr.2014.02.007
Benedetto, A., Calvi, A., & D’Amico, F. (2011). Effects of mobile telephone tasks on
driving performance: A driving simulator study. Advances in Transportation
Studies, (26), 29–44.
Ben-Edigbe, J., Mashros, N., & R, R. (2013). Extent of sight distance reductions
caused by rainfall on single carriageway roads. Int. J. Traffic Transport. Eng.,
3(3), 291–301.
Ben-Edigbe, J., R, R., & Jailani, N. F. (2013). Effects of U-turning maneuvers at
midblock facilities on traffic kinematic waves. Journal of Applied Sciences, 13,
602–608.
Ben-Edige, J., Rahman. R, & Hassan, A. (2013). Effects of weaving, merging and
diverging on driver reactions at Mmidblock U-turn facilities. 13th World
Conference of Transport Research, 1–17.
96
Bottom, C. G., & Ashworth, R. (1978). Factors affecting the variability of driver gap-
acceptance behaviour. Ergonomics, 21(9), 721–734.
Carnis, L., & Blais, E. (2013). An assessment of the safety effects of the French speed
camera program. Accident Analysis and Prevention, 51, 301–309.
Chen, X., Qi, Y., & Liu, G. (2012). An empirical study of gap-acceptance behavior of
right-turn-on-red drivers on dual right-turn lanes. Journal of Transportation
Engineering, 139(February), 173–180.
Cooper, P. J. (1997). The relationship between speeding behaviour (as measured by
violation convictions) and crash involvement. Journal of Safety Research, 28(2),
83–95.
Cooper, S. (2010). Mechanical Law Enforcement: Speeding and Camera Technology.
The Journal of Criminal Law, 74(5), 409–414.
Crundall, D., Clarke, D., Ward, P., & Bartle, C. (2008). Car drivers’ skills and
attitudes to motorcycle safety - A review. London.
Daniel, B. D., Alan, N., & Koorey, G. (2011). Investigating speed patterns and
estimating speed on traffic-calmed streets. IPENZ Transportation Group
Conference Auckland March,.
Darren, E., Jeremy, D., & James, E. (2009). Identifying influences of driving
behaviour: Could the Australian work driving setting be unique?
PROCEEDINGS of the Fifth International Driving Symposium on Human
Factors in Driver Assessment, Training and Vehicle Design, (June), 22–25.
Datta, S. (2014). Modelling Critical Gaps for U-Turn Vehicles At Median Openings
Under Indian Mixed Traffic Conditions - Master Thesis. National Institute of
Technology Rourkela.
Edquist, J., Rudin-Brown, C., & Lenné, M. (2009). Road Design Factors and Their
Interactions with Speed and Speed Limits - Report 298.
Finch, D. J., Kompfner, P., Lockwood, C. R., & Maycock, G. (1994). Project Report
PR 58 - Speed Limits and Accidents. Department of Transport, TRL, Transport
research laboratory.
97
Fleiter, J. J., Lennon, A., & Watson, B. (2010). How do other people influence your
driving speed? Exploring the “who” and the “how” of social influences on
speeding from a qualitative perspective. Transportation Research Part F: Traffic
Psychology and Behaviour, 13(1), 49–62.
Gipps, P. G. (1986). A model for the structure of lane-changing decisions.
Transportation Research Part B: Methodological, 20(5), 403–414.
Guo, T., Liu, P., Lu, J. J., Lu, L., & Cao, B. (2011). Procedure for evaluating the
impacts of indirect driveway left-turn treatments on traffic operations at
signalized intersections. Journal of Transportation Engineering, 137(11), 760–
766.
Guo, Z., Wan, H., Zhao, Y., Wang, H., & Li, Z. (2013). Driving simulation study on
speed-change lanes of the multi-lane freeway interchange. Procedia - Social and
Behavioral Sciences, 96(Cictp), pp 60-69.
Harbeck, E. L., & Glendon, A. I. (2013). How reinforcement sensitivity and perceived
risk influence young drivers’ reported engagement in risky driving behaviors.
Accident Analysis and Prevention, 54, 73–80.
Hofmann, P., Rinkenauer, G., & Gude, D. (2010). Preparing lane changes while
driving in a fixed-base simulator: Effects of advance information about direction
and amplitude on reaction time and steering kinematics. Transportation Research
Part F: Traffic Psychology and Behaviour, 13(4), 255–268.
Hwang, S. Y., & Park, C. H. (2005). Modeling of the gap acceptance behavior at a
merging section of urban freeway. Proceedings of the Eastern Asia Society for
Tansportation Studies, 5, 1641–1656.
Iskandar Regional Development, A. (2011). Road Layout Design Blueprint for
Iskandar Malaysia.
Islam, M. M., & Hadhrami, A. Y. S. Al. (2012). Increased motorization and road traffic
accidents in Oman. Journal of Emerging Trends in Economics and Management
Sciences (JETEMS), 3(6), 907–914.
98
Itoh, M., Yoshimura, K., & Inagaki, T. (2007). Inference of large truck driver’s intent
to change lanes to pass a lead vehicle via analyses of driver’s eye glance behavior
in the real world. SICE Annual Conference 2007, (1), 2385–2389.
Jooho, P., Donghyun, S., & Woon-Sung, L. (2006). A driving simulator study on
adaptive cruise control failure. Proc. SICE-ICASE 2006 International Joint
Conference, 2138–2141.
Jurecki, R. S., & Stańczyk, T. L. (2014). Driver reaction time to lateral entering
pedestrian in a simulated crash traffic situation. Transportation Research Part F:
Traffic Psychology and Behaviour, 27(PA), 22–36.
Kassner, A., & Vollrath, M. (2006). How to assist merging onto the freeway.
Proceedings of the IEEE ITSC 2006, 121–126.
Kearney, J. K., Grechkin, T., Cremer, J., & Plamert, J. (2006). T Raffic Generation for
Studies of Gap Acceptance. Proceedings of the Driving Simulation Conference
(DSC’06), (October), 177–186.
Kesting, A., Treiber, M., & Helbing, D. (2007). General lane-changing model MOBIL
for car-following models. Transportation Research Record, 1999(1), 86–94.
Keyvan-Ekbatani, M., Knoop, V. L., & Daamen, W. (2015). Categorization of the lane
change decision process on freeways. Transportation Research Part C: Emerging
Technologies, 31.
Konishi, K., Kokame, H., & Hirata, K. (1999). Coupled map car-following model and
its delayed-feedback control. Physical Review. E, Statistical Physics, Plasmas,
Fluids, and Related Interdisciplinary Topics, 60(4), 4000–4007.
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research
activities. Educational and Psychological Measurement, 38(1), 607–610.
Kusuma, A., Liu, R., Choudhury, C. F., & Montgomery, F. (2014). Analysis of the
driving behaviour at weaving section using multiple traffic surveillance data.
Transportation Research Procedia, 3(July), 51–59.
L. Pline, J. (1992). Traffic Engineering Handbook. (J. L. Pline, Ed.) (Fourth Edi).
Institute of Transportation Engineers.
99
Leitão, M. J., Moreira, A., Santos, J. a., Sousa, a. A., & Ferreira, F. N. (1999).
Evaluation of driving education methods in a driving simulator. GVE’99
Computer Graphics and Visulalization Education ’99, 153–158.
Li, S., Yang, L., Gao, Z., & Li, K. (2014). Stabilization strategies of a general nonlinear
car-following model with varying reaction-time delay of the drivers. ISA
Transactions, 53(6), 1739–1745.
Liu, R., & Wang, J. (2007). A general framework for the calibration and validation of
car- following models along an uninterupted open highway. Transport, 1–13.
Liu, X. (2011). The maximum safe speed calculation model research on curve.
International Conference on Transportation Information and Safety, Ictis 2011,
1642–1648.
Lo, V. E. W., & Green, P. A. (2013). Development and evaluation of automotive
speech interfaces: Useful information from the human factors and the related
literature. International Journal of Vehicular Technology, 2013, 1–13.
Lv, W., Song, W. G., & Fang, Z. M. (2011). Three-lane changing behaviour simulation
using a modified optimal velocity model. Physica A, 390(12), 2303–2314.
Martínez-Díaz, M., & Pérez, I. (2015). A simple algorithm for the estimation of road
traffic space mean speeds from data available to most management centres.
Transportation Research Part B: Methodological, 75, 19–35.
Mashros, N., Ben-edigbe, J., Alhassan, H. M., & Hassan, S. A. (2014). Jurnal
Teknologi - Investigating the impact of rainfall on travel speed. Research Gate,
3(December), 33–38.
Masirin, M. I., & Mohamad, N. A. (2014). Analysis of road infrastructural audit along
jalan Batu Pahat- Kluang Malaysia : A case study. In International Integrated
Engineering Summit (IIES 2014) (pp. 1–7).
Mason, J. M., Fitzpatrick, K., Seneca, D. L., & Davinroy, T. B. (1992). Identification
of inappropriate driving behaviors. Journal of Transportation Engineering-Asce,
118(2), 281–298.
Md Diah. J., Abdul Rahman, M. Y., & Adnan, M. A. (2008). Traffic data reduction
technique using portable vision based traffic analyser (PVBTA) at Malaysian
100
conventional roundabout. Malaysian Universities Transport Research Forum
Conference (MUTRFC), 2008(August), 1–12.
Md Diah. J., Abdul Rahman, M. Y., Adnan, M. a., & Hooi Ling, K. (2011). Modeling
the relationship between geometric design and weaving section flow process of
conventional roundabouts. Journal of Transportation Engineering, 137(12), 980–
986.
Md Diah. J., Ahmad, J., & Mukri, M. (2013). The methodology on statistical analysis
of data transformation for model development. International Journal of Statistics
and Applications, 2(6), 94–100.
Md Diah. J., Qastalani, N., Akram, M., & Manan, A. (2012). The development of lane
changing model in approaching heavy vehicles at signalized junction.
Md Diah, J., Abdul Rahman, M. Y., & Adnan, M. A. (2007). The impact of driver
driving behaviour on the performance of Malaysian roundabout, (1).
Md Diah, J., Abdul Rahman, M. Y., Adnan, M. A., & Atan, I. (2010). Weaving section
flow model at the weaving area of Malaysian conventional roundabout. Journal
of Transportation Engineering, 136(8), 782–792.
Mehmood, A. (2009). Determinants of speeding behavior of drivers in Al Ain (United
Arab Emirates). Journal of Transportation Engineering, 135(October), 721–729.
Meng, Q., & Weng, J. (2012). Classification and regression tree approach for
predicting drivers’ merging behavior in short-term work zone merging areas.
Journal of Transportation Engineering, 138(8), 1062–1070.
Ministry of Works, M. (2009). Malaysian Highway Capacity Study - Technical report
2.
Montella, A., Aria, M., D’Ambrosio, A., Galante, F., Mauriello, F., & Pernetti, M.
(2010). Perceptual measures to influence operating speeds and reduce crashes at
rural intersections. Transportation Research Record: Journal of the
Transportation Research Board, 2149(1), 11–20.
Montella, A., Claudio, V., & Mauriello, F. (2011). Drivers’ speed behaviour at rural
intersections : Simulator experiment and real world monitoring. Road Safety and
Simulation, 1–14.
101
Montgomery, D., Runger, G., & Faris Hubele, N. (1998). Engineering Statistics. John
Wiley & Sons. Inc.
Moridpour, S., Rose, G., & Sarvi, M. (2010). Effect of surrounding traffic
characteristics on lane changing behavior. Journal of Transportation
Engineering, 136(11), 973–985.
Obaidat, T. I. A., & Elayan, M. S. (2013). Gap acceptance behavior at U-turn median
openings – Case study in Jordan. Jordan Journal of Civil Engineering, 7(3), 332–
341.
Petrucelli, J., Nandram, B., & Chen, M. (1999). Applied Statistics for Engineers and
Scientists. Prentice-Hall, Inc.
Petzoldt, T., Schleinitz, K., Krems, J. F., & Gehlert, T. (2017). Drivers??? gap
acceptance in front of approaching bicycles ??? Effects of bicycle speed and
bicycle type. Safety Science, 92, 283–289.
Pilkington, P., & Kinra, S. (2005). Effectiveness of speed cameras in preventing road
traffic collisions and related casualties: systematic review. BMJ (Clinical
Research Ed.), 330(7487), 331–4.
Prasetijo, J., Razzaly, W., Wu, N., Ambak, K., Sanik, M. E., Rohani, M. M., & Ahmad,
H. (2014). Capacity analysis of priority intersections with flare under mixed
traffic conditions. Procedia - Social and Behavioral Sciences, 138(0), 660–670.
Public Works Department, M. (2015). Arahan Teknik Jalan - A Guide on Geometric
Design of Roads. Kuala Lumpur: Public Work Department of Malaysia.
Rahman. R. (2012). Extent Of traffic kinematic waves and queuing caused by
midblock U-turn facilities. Proceedings of the ITRN2012, 8(August).
Rahman. R, & Ben-edigbe, J. (2015). Jurnal Teknologi - Impact of multilane median
openings zone on travel speed. Technology Journal, 4, 15–20.
Rahman. R, & Hassan, A. (2014). Effect of U-turning at midblock facilities on
weaving intensity. Research Journal of Applied Sciences, Engineering and
Technology, 7(22), 4645–4651.
102
RMP. (2014). Laporan Perangkaan Kemalangan Jalan Raya Malaysia 2014. RoYal
Malaysia Police.
Ryan, T. (2007). Modern Engineering Statistics. John Wiley & Sons. Inc.
Schmidt, K., Beggiato, M., Hoffmann, K. H., & Krems, J. F. (2014). A mathematical
model for predicting lane changes using the steering wheel angle. Journal of
Safety Research, 49(February), 85–90.
Scott-Parker, B., Watson, B., & King J, M. (2013). If they say go faster or something
I’ll probably go faster: peer influence upon the risky driving behaviour of young
novices. In Proceedings of the 2013 Australasian Road Safety Research, Policing
and Education Conference (p. 12p).
Sharma, V. K., Mondal, S., & Gupta, A. (2017). Analysis of u-turning behaviour of
vehicles at mid-block median opening in six lane urban road : A case study.
Internation Journal of Traffic and Transportation Engineering, 7(2), 243–255.
Sheu, J. B., & Wu, H. J. (2015). Driver perception uncertainty in perceived relative
speed and reaction time in car following - A quantum optical flow perspective.
Transportation Research Part B: Methodological, 80, 257–274.
Simons-Morton, B. G., Ouimet, M. C., Chen, R., Klauer, S. G., Lee, S. E., Wang, J.,
& Dingus, T. A. (2012). Peer influence predicts speeding prevalence among
teenage drivers. Journal of Safety Research, 43(5–6), 397–403.
Soole, D. ., Lewis, L., Fleiter, J. J., Sharon, N., & Watson, A. (2008). Improving self-
report measures of speeding. In Accident Research and Road Safety-Queensland
(pp. 1–10).
Soole, D. W., Fleiter, J. J., & Watson, B. (2013). Point-to-point speed enforcement:
recommendations for better practice. In Proceedings of the 2013 Australasian
Road Safety Research, Policing & Education Conference (p. 10p–10p).
Statistic department. (2010). Population Statistic. (Department of Statistics Malaysia,
Ed.). Malaysia.
Summala, H. (2000). Brake reaction times and driver behavior analysis.
Transportation Human Factors, 2(3), 217–226.
103
Tanishita, M., & van Wee, B. (2016). Impact of vehicle speeds and changes in mean
speeds on per vehicle-kilometer traffic accident rates in Japan. International
Association of Traffic and Safety Sciences, 0–5.
Taylor, M. C., D A, L., & Baruya, A. (2011). The Effects of Drivers’ Speed on the
Frequency of Road Accidents. (T. R. Laboratory, Ed.)Transport Research
Foundation Group of Companies. Berkshire.
Tian, E. (2011). Driver characteristics in the lane change process. 2011 International
Conference on Remote Sensing, Environment and Transportation Engineering,
3093–3096.
Tiezhu, L., Jianyou, D., & Wei, W. (2007). Vehicles safety distance on freeway.
International Conference on Transportation AEngineering 2007 (ICTE 2007),
992–997.
Triggs, T. J., & Harris, W. G. (1982). Reaction Time of Drivers to Road Stimuli.
Medicinski Pregled (Vol. 62). Department of Psychology, Monash University,
Victoria 3800, Australia.
Várhelyi, A., Hjälmdahl, M., Hydén, C., & Draskóczy, M. (2004). Effects of an active
accelerator pedal on driver behaviour and traffic safety after long-term use in
urban areas. Accident Analysis and Prevention, 36(5), 729–737.
Wang, J. (2006). A Merging Model for Motorway Traffic. Transportation Research
Record. The University of Leeds.
Wang, J., Liu, R., & Montgomery, F. (2005). A simulation model for motorway
merging behaviour. Transportation and Traffic Theory. Flow, Dynamics, 1–20.
Watson, B., Siskind, V., Fleiter, J. J., Watson, A., & Soole, D. (2015). Assessing
specific deterrence effects of increased speeding penalties using four measures of
recidivism. Accident Analysis and Prevention, 84, 27–37.
Wei, N., & Wanjing, M. (2013). Simulation-based study on a lane assignment
approach for freeway weaving section. Procedia - Social and Behavioral
Sciences, 96(Cictp), 528–537.
Wenzel, T. P., & Ross, M. (2005). The effects of vehicle model and driver behavior
on risk. Accident Analysis and Prevention, 37(3), 479–494.
104
Williams, A. F., Leaf, W. A., Simons-Morton, B. G., & Hartos, J. L. (2006). Vehicles
driven by teenagers in their first year of licensure. Traffic Injury Prevention, 7(1),
23–30.
Wilson, F. A., & Stimpson, J. P. (2010). Trends in fatalities from distracted driving in
the United States, 1999 to 2008. American Journal of Public Health, 100(11),
2213–2219.
Wu, B. F., Chen, Y. H., & Yeh, C. H. (2014). Driving behaviour-based event data
recorder. Iet Intelligent Transport Systems, 8(4), 361–367.
Xiaorui, W., & Hongxu, Y. (2013). A lane Acceration of car following behavior.
Procedia - Social and Behavioral Sciences, 96(Cictp), 2354–2361.
Xue, Q., Wang, B., Zhang, Y., & Yang, X. (2015). Analysis of reaction time during
car-following process based on driving simulation test. The 3rd International
Conference on Transportation Information and Safety, 207–212.
Yeoh, F. S., Benjamin Chan, Y.-F., & Syed Abdul Rashid, S. N. (2011). Predictors of
driving practices among older drivers : Two ethnic groups comparison in
peninsular Malaysia. International Journal of Humanities and Social Science, 1,
174–184.
Yi-zhi, W., Quan, Y., & Yi-bing, L. (2011). Modeling multi-view scenario of driving
simulation on typical side-impact accidents. 2011 International Conference on
Consumer Electronics, Communications and Networks (CECNet), 470–473.
Zhang, J., Zhong, G., Qiao, L., & Ran, B. (2015). Jian Zhang Gang Zhong Li-li.
International Conference of Transportation Professionals, CICTP 2015, 3939–
3947.
Zhao, D., Peng, H., & Nobukawa, K. (2014). Analysis of mandatory and discretionary
lane change behaviors for heavy trucks. AVEC, (Dlc), 355–360.
Zheng, Z., Ahn, S., Chen, D., & Laval, J. (2013). The effects of lane-changing on the
immediate follower: Anticipation, relaxation, and change in driver
characteristics. Transportation Research Part C: Emerging Technologies, 26,
367–379.