Information-based network control strategies consistent with estimated driver behavior

This study proposes a fuzzy control based methodology to determine information-based network control strategies that are consistent with the controller's objectives and its estimation of driver response behavior. It is the core of the broader problem where the objective is to enhance the performance of a vehicular traffic system through real-time information-based network control strategies. The controller seeks behavior consistency by solving a fixed-point problem that estimates drivers' likely reactions to the controller-proposed information strategies while determining them. Experiments are performed to evaluate the effectiveness of the proposed methodology. The results suggest the importance of using a behavior-consistent approach to determine the in formation-based network control strategies. That is, the effects of driver response behavior to information provision may require more meaningful strategies than those provided under the traditional dynamic traffic assignment models to reliably estimate or control system performance. Information strategies that are not behavior-consistent can potentially deteriorate system performance. (C) 2008 Elsevier Ltd. All rights reserved.

[1]  Hong Kam Lo,et al.  Multiclass Dynamic Traffic Assignment Model: Formulation and Computational Experiences , 1996 .

[2]  Alexander Paz,et al.  On-line calibration of behavior parameters for behavior-consistent route guidance , 2009 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[4]  Hani S. Mahmassani,et al.  Comparative analysis of robustness of centralized and distributed network route control systems in incident situations , 1996 .

[5]  Srinivas Peeta,et al.  Behavior-based consistency-seeking models as deployment alternatives to dynamic traffic assignment models , 2006 .

[6]  Alexander Paz,et al.  Behavior-consistent real-time traffic routing under information provision , 2009 .

[7]  Srinivas Peeta,et al.  A Hybrid Deployable Dynamic Traffic Assignment Framework for Robust Online Route Guidance , 2002 .

[8]  Jon Alan Bottom,et al.  Consistent anticipatory route guidance , 2000 .

[9]  Boriana L. Milenova,et al.  Fuzzy and neural approaches in engineering , 1997 .

[10]  Satoshi Fujii,et al.  Drivers’ Learning and Network Behavior: Dynamic Analysis of the Driver-Network System as a Complex System , 1999 .

[11]  G. Dimitrakopoulos,et al.  Intelligent Transportation Systems , 2010, IEEE Vehicular Technology Magazine.

[12]  Markos Papageorgiou,et al.  Simple Decentralized Feedback Strategies for Route Guidance in Traffic Networks , 1999, Transp. Sci..

[13]  Hani S. Mahmassani,et al.  Multiple user classes real-time traffic assignment for online operations: A rolling horizon solution framework , 1995 .

[14]  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.

[15]  C Ajluni.  INTELLIGENT TRANSPORTATION SYSTEMS HIT THE ROAD , 1997 .

[16]  Athanasios K. Ziliaskopoulos,et al.  Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future , 2001 .

[17]  Alexander Paz,et al.  Fuzzy Control Model Optimization for Behavior-Consistent Traffic Routing Under Information Provision , 2008, IEEE Transactions on Intelligent Transportation Systems.

[18]  Shlomo Bekhor,et al.  Evaluation of choice set generation algorithms for route choice models , 2006, Ann. Oper. Res..