A Hybrid Model for Driver Route Choice Incorporating En-Route Attributes and Real-Time Information Effects

The en-route driver behavior problem under information provision is characterized by subjective and linguistic variables, in addition to situational factors. Fuzzy modeling provides a robust mechanism to capture subjectivity and/or the linguistic nature of the causal variables. This motivates the development of a hybrid en-route route choice model that combines quantitative and fuzzy variables to more robustly predict driver routing decisions under information provision. Simulation experiments are conducted to analyze the ability of the hybrid model to capture en-route driver behavior effects in the within-day and day-to-day contexts.

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

[2]  Samer Madanat,et al.  Perception updating and day-to-day travel choice dynamics in traffic networks with information provision , 1998 .

[3]  Ryuichi Kitamura,et al.  Route Choice Model with Inductive Learning , 2000 .

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

[5]  Gwo-Hshiung Tzeng,et al.  Using a weight-assessing model to identify route choice criteria and information effects , 2001 .

[6]  S. Peeta,et al.  Data-Consistent Fuzzy Approach for Online Driver Behavior Under Information Provision , 2002 .

[7]  T Lotan,et al.  Effects of familiarity on route choice behavior in the presence of information , 1997 .

[8]  T. Lotan,et al.  Modeling discrete choice behavior based on explicit information integration and its application to the route choice problem , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[9]  S. Peeta,et al.  Adaptability of a hybrid route choice model to incorporating driver behavior dynamics under information provision , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[11]  A. Palma,et al.  The impact of adverse weather conditions on the propensity to change travel decisions: A survey of Brussels commuters , 1997 .

[12]  G. Pang,et al.  Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches , 1995, Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future.

[13]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[14]  Haris N. Koutsopoulos,et al.  Modeling default behavior in the presence of information and its application to the route choice problem , 1999 .

[15]  Hani S. Mahmassani,et al.  Modeling Inertia and Compliance Mechanisms in Route Choice Behavior Under Real-Time Information , 2000 .

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

[17]  Srinivas Peeta,et al.  Real-Time Variable Message Sign–Based Route Guidance Consistent with Driver Behavior , 2001 .

[18]  Satoshi Fujii,et al.  Anticipated Travel Time, Information Acquisition, and Actual Experience: Hanshin Expressway Route Closure, Osaka-Sakai, Japan , 2000 .

[19]  Didier Dubois,et al.  Default Reasoning and Possibility Theory , 1988, Artif. Intell..