Leave the expressway or not? Impact of dynamic information

This study investigates drivers’ diversion decision behavior under expressway variable message signs that provide travel time of both an expressway route and a local street route. Both a conventional cross-sectional logit model and a mixed logit model are developed to model drivers’ response to travel time information. It is based on the data collected from a stated preference survey in Shanghai, China. The mixed logit model captures the heterogeneity in the value of “travel time” and “number of traffic lights” and accounts for correlations among repeated choices of the same respondent. Results show that travel time saving and driving experience serve as positive factors, while the number of traffic lights on the arterial road, expressway use frequency, being a middle-aged driver, and being a driver of an employer-provided car serve as negative factors in diversion. The mixed logit model obviously outperforms the cross-sectional model in dealing with repeated choices and capturing heterogeneity regarding the goodness-of-fit criterion. The significance of standard deviations of random coefficients for travel time and number of traffic lights evidences the existence of heterogeneity in the driver population. The findings of this study have implications for future efforts in driver behavior modeling and advanced traveler information system assessment.

[1]  M. Taniguchi,et al.  Incorporating an information acquisition process into a route choice model with multiple information sources , 1999 .

[2]  M. Ben-Akiva,et al.  Discrete choice analysis , 1989 .

[3]  Lijun Sun,et al.  Advanced traveler information system for metropolitan expressways in Shanghai, China , 2006 .

[4]  Yang Bai,et al.  Why do people change routes? Impact of information services , 2013, Ind. Manag. Data Syst..

[5]  Caspar G. Chorus,et al.  Traveler response to information , 2007 .

[6]  Ka-Hung Lai,et al.  SP APPROACH TOWARD DRIVER COMPREHENSION OF MESSAGE FORMATS ON VMS , 2000 .

[7]  P. Nijkamp,et al.  Variable message signs and radio traffic information: An integrated empirical analysis of drivers' route choice behaviour , 1996 .

[8]  M. Ben-Akiva,et al.  Cognitive cost in route choice with real-time information: An exploratory analysis , 2011 .

[9]  Yu-Hsin Liu,et al.  Route switching behavior on freeways with the provision of different types of real-time traffic information , 2005 .

[10]  Hani S. Mahmassani,et al.  DYNAMICS OF COMMUTING DECISION BEHAVIOR UNDER ADVANCED TRAVELER INFORMATION SYSTEMS , 1999 .

[11]  Jeffrey M. Wooldridge,et al.  Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .

[12]  Constantinos Antoniou,et al.  Development of a Mixed Multi-Nomial Logit Model to Capture the Impact of Information Systems on Travelers' Switching Behavior , 2007, J. Intell. Transp. Syst..

[13]  J Lappin,et al.  UNDERSTANDING AND PREDICTING TRAVELER RESPONSE TO INFORMATION: A LITERATURE REVIEW , 2001 .

[14]  Nick Hounsell,et al.  Driver response to variable message sign information in London , 2002 .

[15]  Asad J. Khattak,et al.  Commuters' enroute diversion and return decisions: Analysis and implications for advanced traveler information systems , 1993 .

[16]  C. Bhat Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model , 2001 .

[17]  K. Train Discrete Choice Methods with Simulation , 2003 .

[18]  Mario Marinelli,et al.  Modeling risk perception in ATIS context through Fuzzy Logic , 2011 .

[19]  Peter Nijkamp,et al.  Behavioural and Network Impacts of Driver Information Systems , 1999 .

[20]  Paul P Jovanis,et al.  Driver En Route Guidance Compliance and Driver Learning with Advanced Traveler Information Systems: Analysis with Travel Simulation Experiment , 2003 .

[21]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[22]  Mohamed A. Abdel-Aty,et al.  Examination of Multiple Mode/Route-Choice Paradigms Under ATIS , 2006, IEEE Transactions on Intelligent Transportation Systems.

[23]  Mohamed Abdel-Aty,et al.  Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function , 2004 .

[24]  Mohamed Abdel-Aty,et al.  USING STATED PREFERENCE DATA FOR STUDYING THE EFFECT OF ADVANCED TRAFFIC INFORMATION ON DRIVERS' ROUTE CHOICE , 1997 .

[25]  Hongcheng Gan Graphical route information panel for the urban freeway network in Shanghai, China , 2010 .

[26]  Hai Yang,et al.  Exploration of route choice behavior with advanced traveler information using neural network concepts , 1993 .

[27]  Hani S. Mahmassani,et al.  EXPERIMENTAL INVESTIGATION OF ROUTE AND DEPARTURE TIME CHOICE DYNAMICS OF URBAN COMMUTERS , 1988 .

[28]  Zhong-Ren Peng,et al.  Motorist Response to Arterial Variable Message Signs , 2004 .

[29]  Jorge Ramos,et al.  Content of Variable Message Signs and On-Line Driver Behavior , 2000 .

[30]  Eran Ben-Elia,et al.  Which road do I take? A learning-based model of route-choice behavior with real-time information , 2010 .

[31]  Ian Palmer,et al.  Validating the results of a route choice simulator Transportation Research C 5 , 1997 .

[32]  Peter Bonsall,et al.  Driver response to variable message signs: a stated preference investigation , 1997 .

[33]  Haris N. Koutsopoulos,et al.  A driving simulator and its application for modeling route choice in the presence of information , 1994 .

[34]  Hongcheng Gan,et al.  Urban freeway user' diversion response to variable message sign displaying the travel time of both freeway and local street , 2012 .

[35]  Hani S. Mahmassani,et al.  Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation , 2003 .