hes s or

212 Pages·2007·13.58 MB·English

Off-line Calibration of Dynamic Traffic Assignment Models
by
Ramachandran Balakrishna
Bachelor of Technology in Civil Engineering
Indian Institute of Technology, Madras, India (1999)
Master of Science in Transportation
Massachusetts Institute of Technology (2002)
Submitted to the Department of Civil and Environmental Engineering
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in the Field of Transportation Systems
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2006
( Massachusetts Institute of Technology 2006. All rights reserved.
Autho..r. ........................ ,._.: ................... ...
Department of Civil and Environmental Engineering
^\ ,A May 12, 2006
Certifiedb y. ................. /........... .....
Moshe E. Ben-Akiva
Edmund K. Turner Professor of Civil and Environmental Engineering
,/ hes s or
rull IU--- ................. .-......-.. r- -Ha- -s ..ei . ~o
Haris N'.Koutsopoulos
Associate Professor of Civil and Environmental Engineering,
Northeastern University
.~~lessBupisor
N- r t
Acceptedb y................. 1 4
$11g..........$..7
Xndrew' Q
Chairman, Departmental Committee for Graduate Students
OF TECHNOLOGY
ARCHIVES
F f 7 M0 1]
JV's . wv
l I
LIBRARIES

Off-line Calibration of Dynamic Traffic Assignment Models
by
Ramachandran Balakrishna
Submitted to the Department of Civil and Environmental Engineering
on May 12, 2006, in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy in the Field of Transportation Systems
Abstract
Advances in Intelligent Transportation Systems (ITS) have resulted in the deployment
of surveillance systems that automatically collect and store extensive network-wide
traffic data. Dynamic Traffic Assignment (DTA) models have also been developed for
a variety of dynamic traffic management applications. Such models are designed to
estimate and predict the evolution of congestion through detailed models and algo-
rithms that capture travel demand, network supply and their complex interactions.
The availability of rich time-varying traffic data spanning multiple days thus provides
the opportunity to calibrate a DTA model's many inputs and parameters, so that its
outputs reflect field conditions.
The current state of the art of DTA model calibration is a sequential approach, in
which supply model calibration (assuming known demand inputs) is followed by de-
mand calibration with fixed supply parameters. In this thesis, we develop an off-line
DTA model calibration methodology for the simultaneous estimation of all demand
and supply inputs and parameters, using sensor data. We adopt a minimization for-
mulation that can use any general traffic data, and present approaches to solve the
complex, non-linear, stochastic optimization problem. Case studies with DynaMIT,
a DTA model with traffic estimation and prediction capabilities, are used to demon-
strate and validate the proposed methodology. A synthetic traffic network with known
demand parameters and simulated sensor data is used to illustrate the improvement
over the sequential approach, the ability to accurately recover underlying model pa-
rameters, and robustness in a variety of demand and supply situations. Archived
sensor data and a network from Los Angeles, CA are then used to demonstrate scal-
ability. The benefit of the proposed methodology is validated through a real-time
test of the calibrated DynaMIT's estimation and prediction accuracy, based on sen-
sor data not used for calibration. Results indicate that the simultaneous approach
significantly outperforms the sequential state of the art.
Thesis Supervisor: Moshe E. Ben-Akiva
Title: Edmund K. Turner Professor of Civil and Environmental Engineering
3
Thesis Supervisor: Haris N. Koutsopoulos
Title: Associate Professor of Civil and Environmental Engineering, Northeastern
University
4
Acknowledgments
This thesis would not have been possible without contributions from various quar-
ters. Foremost, I would like to acknowledge the support and inputs from my thesis
supervisors, Professors Moshe Ben-Akiva and Haris N. Koutsopoulos. They have set
extremely high standards for me, and have taught by example.
My doctoral committee has been an invaluable source of suggestions, advice and
encouragement. I would like to thank Prof. Nigel Wilson, Dr Kalidas Ashok and Dr
Tomer Toledo for their support and guidance.
Faculty and friends have contributed immensely through informal discussions out-
side the classroom/lab. Their genuine interest in my work has been a source of en-
couragement, and has helped me place this research in perspective. I thank Professors
Nigel Wilson, Patrick Jaillet, Cindy Barnhart, Joe Sussman, Ikki Kim, Michel Bier-
laire and Brian Park, and PhD students Hai Jiang and Yang Wen, for their insights.
Other friends including Dr Arvind Sankar, Prof. Lakshmi Iyer, Dr K. V. S. Vinay
and lab-mates Vikrant Vaze and Varun Ramanujam have routinely buttonholed me
on my latest results, which has helped me clarify concepts in my own mind.
I am grateful to Dr Henry Lieu and Raj Ghaman of the Federal Highway Ad-
ministration, whose funding supported much of this research. The data for the Los
Angeles analysis was provided by the California PATH program, and Gabriel Murillo
and Verej Janoyan of the LA Department of Transportation. The tireless Dr Scott
Smith of the Volpe Center was instrumental in getting the outputs from this thesis
out into the real world.
CEE has a long list of able administrators who have cheerfully and pro-actively
attended to many a potential issue before they arose. I would especially like to thank
Leanne Russell, Anthee Travers, Cynthia Stewart, Donna Hudson, Pat Dixon, Pat
Glidden, Ginny Siggia and Sara Goplin for their constant assistance that resulted in
a smooth run through grad school.
Lab-mates come and go, but the memories will live on forever. I shared an office
and many cherished moments with Dr Constantinos Antoniou, who continues to be
5
my fountain of knowledge on a wide range of transportation and IT topics. Together,
we proved the sufficiency of plain, vanilla e-mail for high-volume, real-time communi-
cations across continents. Bhanu Prasad Mahanti, Ashish Gupta, Anita Rao, Gunwoo
Lee, Akhil Chauhan, Charisma Choudhury, Vaibhav Rathi, Vikrant Vaze, Varun Ra-
manujam, Maya Abou Zeid, Emmanuel Abbe, Caspar Chorus, Carmine Gioia and
Gianluca Antonini have all made the ITS lab a vibrant and social environment.
Room-mates Prahladh Harsha, Jeff Hwang and Rajappa Tadepalli provided some-
thing to look forward to upon returning home from the lab. At least until I got married
and moved out! Friends made at MIT and before have watched out for me in various
ways. I thank Arvind Sankar, Lakshmi Iyer, K. V. S. Vinay, Bharath Krishnan, Ra-
jappa Tadepalli and Padmashree Ramachandran, Aravind Srinivasan and Karunya
Ramasamy, Srini Sundaram, Anand Sivaraman and Chaitanya Ullal for their help
and companionship.
I thank Vikrant Agnihotri, Vikram Sivakumar and Varun Ramanujam for their
diligent organization of cricket games at MIT, and everyone who showed up so that
we not only had quorum, but also an enthusiastic knock-about.
I am grateful to Radha Kalluri, Ray Goldsworthy, Aarthi Chandrasekharan, Sri-
nath Gaddam, Charu Varadharajan and everybody else at (and associated with) MIT
Natya for providing a cultural edge to my MIT stay (not to mention the motivation to
pick up my violin a few times). I thank Kripa Varanasi, Prahladh Harsha and Ganesh
Davuluri for volunteering as guinea pigs in my experiments as a violin teacher.
I cannot imagine how I would have finished this project without the whole-hearted
support of my family: my wife Krithika, parents and grandparents and sister Poorn-
ima have stood by me throughout.
And finally, my thanks to God for the strength to survive and experience this
great journey.
6
Contents
I Introduction 15
..................
1.1 Generic structure of DTA models .................. 20
1.2 Typology of DTA Models ..... .................. 20
1.2.1 Early developments. .................. 21
1.2.2 Analytical approaches . .................. 23
1.2.3 Simulation-based approaches . .................. 26
1.2.4 Synthesis. .................. 29
1.3 Motivation and scope . .................. 30
1.4 Problem definition. .................. 34
1.5 Thesis organization .......... 36
2 Literature review 37
2.1 DTA calibration literature ....................... 38
2.2 Demand-supply calibration of DTA models .............. 39
2.3 Estimation of supply models ...................... 44
2.3.1 Macroscopic and mesoscopic supply calibration ....... 44
2.3.2 Microscopic supply calibration ................. 47
2.4 Estimation of demand models ..................... 50
2.4.1 Travel behavior modeling .................... 51
2.4.2 The OD estimation problem .................. 53
2.4.3 Joint estimation of OD demand and travel behavior models. 61
2.5 Conclusions: state-of-the-art (reference case) .......... 63
2.6 Summary ................................ 64
7
3 Methodology 67
3.1 Calibration variables . . . . . . . . . . . . . . . . . . ..68
3.2 Sensor data for calibration ............. ... ... .. ... . 69
3.3 The historical database ............... . . . .. . . . . .. . .72
............ .76
3.4 General problem formulation ............
............ .80
3.5 Problem characteristics ...............
............ .80
3.5.1 Large scale.
3.5.2 Non-linearity ................ . . . .. . . . . ... .81
3.5.3 Non-analytical simulator output ...... . . . . . . . . . ... 81
3.5.4 Stochasticity ................ . . . .. . . . . ... .82
3.6 Review of optimization methods .......... . . . . . . . . . ... .83
3.6.1 Path search methods ............ . . . . . . . . . ... 84
3.6.2 Pattern search methods .......... . . . . . . . . . ... .94
3.6.3 Random search methods .......... . . . . . . . . . ... 98
........... .101
3.6.4 Summary ..................
........... .102
3.7 Solution of the off-line calibration problem ....
........... .102
3.7.1 Combined Box-SNOBFIT algorithm . . .
3.7.2 Some practical algorithmic considerations ........... .103
........... .105
3.8 Summary ......................
4 Synthetic Case Study 107
4.1 Objectives ......................... 108
. . . . . . . .
4.2 Experimental setup .................... . . . . . . . . 108
4.2.1 Sensor dataset generation ............ . . . . . . . . 108
4.2.2 Overview of DTA model and parameters .... . . . . . . . . 109
4.2.3 Network description and calibration variables . . . . . . . . 111
4.3 Base case analysis ..................... . . . . . . . . 113
4.3.1 Estimators ..................... . . . . . . . . 115
4.3.2 Measures of performance ............. . . . . . . . . 116
4.3.3 Numerical results using Box-SNOBFIT ..... . . . . . . . . 117
8
..............
4.4 Sensitivity analysis .............. .............. 121
4.4.1 Factor levels and runs ........ .............. 121
4.4.2 Numerical results........... .............. 123
4.4.3 Conclusions and further analysis ... .............. 127
4.5 Base case numerical results with SPSA . . . .............. 128
4.5.1 Scalability: Box-SNOBFIT vs. SPSA .............. 132
4.5.2 Conclusions ............ . .............. 135
4.6 Synthesis of results and contributions .... 136
5 Case Study 139
5.1 Objectives ................... . .. . . . . . . . . . . . 140
5.2 The Los Angeles dataset ........... . . . . . . . . . . . 141 ..
5.2.1 Network description ......... . . . . . . . . . . . 141 ..
5.2.2 Surveillance data. . . . . . . . . . . . . 142 .
5.2.3 Special events and weather logs . . . 143
5.2.4 The historical database ........ . .. . . . . . . . . . . . 143
5.3 Application .................. . .. . . . . . . . . . . . 144
5.3.1 Reference case ............. . . . . . . . . . . . . 144 .
5.3.2 Network setup and parameters .... . .. . . . . . . . . . . . 146
5.3.3 Estimators ............... . . . . . . . . . . . . 147 .
5.3.4 Measures of performance ....... .. . . . . . . . . . . . . 148
5.3.5 Solution algorithm. . . . . . . . . . . . 148 ...
5.4 Results ..................... . . . . . . . . . . . 150 ...
5.4.1 Calibration results. . . . . . . . . . . . 150 ...
5.4.2 Validation results ........... . . . . . . . . . . . 153 ...
5.5 Synthesis of results and major findings ................ 160
6 Conclusion 167
6.1 Summary. 168
6.2 Research contributions. 169
6.3 Future research directions. 170
9
6.3.1 Equilibrium and day-to-day effects . . . . 170
6.3.2 Observability and optimal sensor coverage 171
6.3.3 Impact of incidents ............. 172
6.3.4 Historical database updating ........ 172
6.3.5 Networks, models and modeling error . . . 173
6.3.6 More detailed travel behavior models . . . 173
6.3.7 Emerging traffic data ............ 174
6.4 Conclusion ...................... 174
A Overview of the DynaMIT System 175
A.1 Overview of DynaMIT-R ........... . . . . . . . . . . . 176 ...
A.1.1 Features and Functionality ...... . . . . . . . . . . . 177 ...
A.1.2 Overall Framework .......... . . . . . . . . . . . 177 ...
A.1.3 Prediction and Guidance Generation . . . . . . . . . . . 183 ...
A.2 Overview of DynaMIT-P ........... . . . . . . . . . . . 184 ...
A.2.1 Features and Functionality ...... . . . . . . . . . . . 185 ...
A.2.2 Overall Framework .......... . . . . . . . . . . . 186 ...
B Prototypical Evaluation: Detailed Numerical Results 193
B.1 Fit to counts, speeds and OD flows ........... 193
Bibliography 197
10