Modeling Drivers' Acceleration and Lane Changing Behavior

189 Pages·1999·1.01 MB·English

Modeling Drivers’ Acceleration and Lane
Changing Behavior
by
Kazi Iftekhar Ahmed
B. Sc. Eng. (Civil)
Bangladesh Univ. of Eng. and Technology (BUET), Dhaka, Bangladesh (1991)
M.S. in Transportation
Massachusetts Institute of Technology, Cambridge, MA (1996)
Submitted to the Department of Civil and Environmental Engineering
in partial ful(cid:12)llment of the requirements for the degree of
Doctor of Science in
Transportation Systems and Decision Sciences
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
February 1999
c Massachusetts Institute of Technology 1999. All rights reserved.
(cid:13)
Author
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Department of Civil and Environmental Engineering
January 8, 1999
Certi(cid:12)ed by
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Moshe E. Ben-Akiva
Professor of Civil and Environmental Engineering
Thesis Supervisor
Certi(cid:12)ed by
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dr. Haris N. Koutsopoulos
Operations Research Analyst
Thesis Supervisor
Accepted by
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Andrew J. Whittle
Chairman, Departmental Committee on Graduate Studies

Modeling Drivers’ Acceleration and Lane
Changing Behavior
by
Kazi Iftekhar Ahmed
Submitted to the Department of Civil and Environmental Engineering
on January 8, 1999, in partial ful(cid:12)llment of the
requirements for the degree of
Doctor of Science in
Transportation Systems and Decision Sciences
Abstract
This thesis contributes to the development of microscopic tra(cid:14)c performance models
which includes the acceleration and lane changing models. It enhances the existing
models and develops new ones. Another major contribution of this thesis is the
empirical work, i.e., estimating the models using statistically rigorous methods and
microscopic data collected from real tra(cid:14)c.
Theaccelerationmodelde(cid:12)nestworegimesoftra(cid:14)c(cid:13)ow: thecar{followingregime
and the free{(cid:13)ow regime. In the car{following regime, a driver is assumed to fol-
low his/her leader, while in the free{(cid:13)ow regime, a driver is assumed to try to at-
tain his/her desired speed. A probabilistic model, that is based on a time headway
threshold, is used to determine the regime the driver belongs to. Heterogeneity across
drivers is captured through the headway threshold and reaction time distributions.
The parameters of the car{followingand free{(cid:13)ow acceleration models along with the
headway threshold and reaction time distributions are jointly estimated using the
maximum likelihood estimation method.
The lane changing decision process is modeled as a sequence of three steps: de-
cision to consider a lane change, choice of a target lane, and gap acceptance. Since
acceptable gaps are hard to (cid:12)nd in a heavily congested tra(cid:14)c, a forced mergingmodel
that captures forced lane changing behavior and courtesy yielding is developed. A
discretechoicemodelframeworkisusedtomodeltheimpactofthesurroundingtra(cid:14)c
environment and lane con(cid:12)guration on drivers’ lane changing decision process.
The models are estimated using actual tra(cid:14)c data collected from Interstate 93 at
the Central Artery, located in downtown Boston, MA, USA. In addition to assessing
the model parameters from statistical and behavioral standpoints, the models are
validated using a microscopic tra(cid:14)c simulator. Overall, the empirical results are
encouraging, and demonstrate the e(cid:11)ectiveness of the modeling framework.
Thesis Supervisor: Moshe E. Ben-Akiva
Title: Professor of Civil and Environmental Engineering
Massachusetts Institute of Technology
Thesis Supervisor: Dr. Haris N. Koutsopoulos
Title: Operations Research Analyst
Volpe National Transportation Systems Center
Cambridge, MA, USA.
4
To
Abbu, Ammu,
my son, Sabih,
and
my wife, Lubna
5
Thesis Committee
Moshe E. Ben-Akiva (Chairman)
Professor
Department of Civil and Environmental Engineering
Massachusetts Institute of Technology
Haris N. Koutsopoulos
Operations Research Analyst
Volpe National Transportation Systems Center
Ismail Chabini
Assistant Professor
Department of Civil and Environmental Engineering
Massachusetts Institute of Technology
Mithilesh Jha
Research Associate
Center for Transportation Studies
Massachusetts Institute of Technology
6
Acknowledgments
I acknowledge with deep sense of gratitude the guidance, invaluable advice, and con-
stant inspiration provided by my supervisors Prof. Moshe Ben{Akiva and Dr. Haris
Koutsopoulos during the course of my studies. I feel privilegedto get the opportunity
to work with them for the last (cid:12)ve years. I have learned a lot from them during the
course of this research.
I am grateful to the other members of my dissertation committee|Prof. Ismail
Chabini and Dr. Mithilesh Jha, for their advice, feed back, and inspiration during
the course of this research.
My special thanks goes to the following individuals without whose contribution
this thesis could not be completed: Dr. Qi Yang, Dr. Kalidas Ashok, Prof. Rabi
Mishalani, Prof. Michel Bierlaire, Alan Chachich, Dave Cuneo, Masroor Hasan, Dr.
Owen Chen, Russel Spieler, Tania Amin, Khwaja Ehsan, Shahnaz Islam, and Prof.
Sha(cid:12)qul Islam.
I am also thankful to the CA/T project at the ITS Research Program for (cid:12)nan-
cially supporting my (cid:12)ve years of studies at MIT.
I would like to thank my friends, fellow students, and administrative sta(cid:11) at
the CEE Department, CTS, and ITS O(cid:14)ce, that made my life at MIT an enjoyable
experience, especially,Adriana,Amalia,Andras,Atul,Bruno,Cheryl,Chris,Cynthia,
Denise, Didier, Dinesh, Dale, Deiki, Dong, Hari, Hong, Je(cid:11), Jessei, John, Jon, Joan,
Juli, Krishna, Lisa, Mark, Masih, Nagi, Niranjan, Pat, Paula, Peter, Prodyut Da,
Shenoi, Scott, Sreeram, Sridevi, Sudhir, Susan, Tomer, Winston, and Yan.
Thanks are due to fellow Bangladeshis Adnan, Fahria and Zeeshan, Minu and
Monjur, Oni and Arif, Rima, Rita and Mukul, Rumi and Saquib, Sabah and Mah-
mood, and Shampa and Sabet, for their friendship and support.
Finally, I wish I knew a better way to express my indebtedness to my wife, Lubna,
my three year old son, Sabih, for their unconditional support, endless love, to my
parents for their encouragement and inspiration throughout my life that helped me
outgrow again and again.
7
Contents
1 Introduction 18
1.1 The Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3 Thesis Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4 Thesis Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Literature Review 25
2.1 Acceleration Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1.1 Car{Following Models . . . . . . . . . . . . . . . . . . . . . . 26
2.1.2 General Acceleration Models . . . . . . . . . . . . . . . . . . . 34
2.1.3 Estimation of the Brake Reaction Time . . . . . . . . . . . . . 37
2.2 Lane Changing Models . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.2.1 Gap Acceptance Models . . . . . . . . . . . . . . . . . . . . . 42
2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3 The Acceleration Model 46
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2 The Acceleration Model . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2.1 The Car{Following Model . . . . . . . . . . . . . . . . . . . . 49
3.2.2 The Free{Flow Acceleration Model . . . . . . . . . . . . . . . 54
3.2.3 The Headway Threshold Distribution . . . . . . . . . . . . . . 56
3.2.4 The Reaction Time Distribution . . . . . . . . . . . . . . . . . 57
8
3.3 Likelihood Function Formulation . . . . . . . . . . . . . . . . . . . . 59
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4 The Lane Changing Model 63
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2 The Lane Changing Model . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.1 Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . 65
4.2.2 Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . 67
4.2.3 Likelihood Function Formulation . . . . . . . . . . . . . . . . 71
4.2.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3 The Forced Merging Model . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.1 Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . 76
4.3.2 Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . 77
4.3.3 Likelihood Function Formulation . . . . . . . . . . . . . . . . 78
4.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5 Data Requirements for Estimating Driver Behavior Models 85
5.1 Methodology for Estimating Instantaneous Speed and Acceleration
from Discrete Trajectory Data . . . . . . . . . . . . . . . . . . . . . . 86
5.1.1 The Local Regression Procedure . . . . . . . . . . . . . . . . . 87
5.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.2.1 Description of the Data Collection Site . . . . . . . . . . . . . 91
5.2.2 Video Processing Software . . . . . . . . . . . . . . . . . . . . 93
5.2.3 Processing the Tra(cid:14)c Data . . . . . . . . . . . . . . . . . . . 94
5.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6 Estimation Results 107
6.1 Estimation Results of the Acceleration Model . . . . . . . . . . . . . 107
6.1.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.2 Estimation Results of the Lane Changing Model . . . . . . . . . . . . 121
9
6.2.1 Estimation Results of the Discretionary Lane Changing Model 121
6.2.2 Estimation Results of the Mandatory Lane Changing Model . 133
6.2.3 Estimation Results of the Forced Merging Model . . . . . . . 140
6.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
7 Model Validation Using a Microscopic Tra(cid:14)c Simulator 146
7.1 MITSIM: a Microscopic Tra(cid:14)c Simulator . . . . . . . . . . . . . . . . 147
7.1.1 The Acceleration Model . . . . . . . . . . . . . . . . . . . . . 148
7.1.2 The Lane Changing Model . . . . . . . . . . . . . . . . . . . . 149
7.2 Validation Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 153
7.2.1 Number of Replications . . . . . . . . . . . . . . . . . . . . . 153
7.2.2 Measures of Goodness{of{(cid:12)t . . . . . . . . . . . . . . . . . . . 154
7.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
7.3.1 The Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
7.3.2 Tra(cid:14)c Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
7.3.3 O{D Estimation from Tra(cid:14)c Counts . . . . . . . . . . . . . . 160
7.3.4 MITSIM Modi(cid:12)cations . . . . . . . . . . . . . . . . . . . . . . 163
7.3.5 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . 164
7.3.6 Validation Results . . . . . . . . . . . . . . . . . . . . . . . . 164
7.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8 Conclusions and Future Research Directions 173
8.1 Summary of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.1.1 The Acceleration Model . . . . . . . . . . . . . . . . . . . . . 173
8.1.2 The Lane Changing Model . . . . . . . . . . . . . . . . . . . . 175
8.1.3 Validation by Microsimulation . . . . . . . . . . . . . . . . . . 176
8.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
8.3 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . 178
8.3.1 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
8.3.2 Estimation and Validation . . . . . . . . . . . . . . . . . . . . 179
8.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
10