Route Choice Behavior in a Driving Simulator With Real-time Information

This research studies travelers' route choice behavior in a driving simulator with real-time information en-route. The authors investigate whether travelers plan strategically for real-time information en-route or simply select a fixed path from origin to destination at the beginning of a trip, and whether network complexity and a parallel driving task affect subjects' strategic thinking ability. In this study, strategic thinking refers to a traveler's route choice decision taking into account future diversion possibilities downstream enabled by information at the diversion node. All of the subjects in this study participated in driving-simulator-based tests while half of the subjects participated in additional PC-based tests. Three types of maps were used. The first type required a one-time choice at the beginning of a trip to test the traveler's risk attitude. The other two types offered route choices both at the beginning of and during a trip to test the traveler's strategic thinking. The study shows that a significant portion of route choice decisions are strategic in a realistic driving simulator environment. Furthermore, different network complexities impose different cognitive demands on a subject and affect his/her strategic thinking ability. A subject tends to be more strategic in a simple network. Lastly, a parallel driving task does not significantly affect a subject's strategic thinking ability. This seemingly counter-intuitive conclusion might be caused by the simplicity of the tested network.

[1]  Michael Scott Ramming,et al.  NETWORK KNOWLEDGE AND ROUTE CHOICE , 2002 .

[2]  Michel Bierlaire,et al.  Capturing correlation with subnetworks in route choice models , 2007 .

[3]  S. Travis Waller,et al.  On the online shortest path problem with limited arc cost dependencies , 2002, Networks.

[4]  Randolph W. Hall,et al.  The Fastest Path through a Network with Random Time-Dependent Travel Times , 1986, Transp. Sci..

[5]  Donald L. Fisher,et al.  The Framing of Drivers' Route Choices when Travel Time Information Is Provided under Varying Degrees of Cognitive Load , 2000, Hum. Factors.

[6]  Shlomo Bekhor,et al.  Adaptation of Logit Kernel to Route Choice Situation , 2002 .

[7]  Daniel T. Gilbert,et al.  Thinking lightly about others: Automatic components of the social inference process. , 1989 .

[8]  Shlomo Bekhor,et al.  Link-Nested Logit Model of Route Choice: Overcoming Route Overlapping Problem , 1998 .

[9]  F. Koppelman,et al.  TRAVEL-CHOICE BEHAVIOR: MODELS OF PERCEPTIONS, FEELINGS, PREFERENCE, AND CHOICE , 1980 .

[10]  Song Gao,et al.  Strategic Thinking and Risk Attitudes in Route Choice , 2010 .

[11]  Daniele Pretolani,et al.  A directed hypergraph model for random time dependent shortest paths , 2000, Eur. J. Oper. Res..

[12]  E. Cascetta,et al.  A MODIFIED LOGIT ROUTE CHOICE MODEL OVERCOMING PATH OVERLAPPING PROBLEMS. SPECIFICATION AND SOME CALIBRATION RESULTS FOR INTERURBAN NETWORKS , 1996 .

[13]  N. Sanders,et al.  Journal of behavioral decision making: "The need for contextual and technical knowledge in judgmental forecasting", 5 (1992) 39-52 , 1992 .

[14]  D. Gilbert.  How mental systems believe. , 1991 .

[15]  H. Mahmassani,et al.  Least expected time paths in stochastic, time-varying transportation networks , 1999 .

[16]  Moshe Ben-Akiva,et al.  Adaptive Route Choice Models in Stochastic Time-Dependent Networks , 2008 .

[17]  Barbara Kanninen,et al.  INTELLIGENT TRANSPORTATION SYSTEMS: AN ECONOMIC AND ENVIRONMENTAL POLICY ASSESSMENT , 1996 .

[18]  Song Gao,et al.  Optimal routing policy problems in stochastic time-dependent networks , 2006 .

[19]  Kan Chen,et al.  Advanced Traveler Information Systems , 2002 .

[20]  M. Bierlaire,et al.  Discrete Choice Methods and their Applications to Short Term Travel Decisions , 1999 .

[21]  Jan Theeuwes,et al.  Driving Simulator Validity: Some Considerations , 1996 .

[22]  Stephen M. Johnson,et al.  The A ect Heuristic in Judgments of Risks and Bene ® ts , 2000 .

[23]  John N. Tsitsiklis,et al.  Stochastic shortest path problems with recourse , 1996, Networks.

[24]  Adaptive least-expected time paths in stochastic, time-varying transportation and data networks , 2001 .

[25]  Mike McDonald,et al.  Effectiveness of using variable message signs to disseminate dynamic traffic information: Evidence from field trails in European cities , 2004 .

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

[27]  Elise Miller-Hooks,et al.  Adaptive least-expected time paths in stochastic, time-varying transportation and data networks , 2001, Networks.

[28]  Song Gao,et al.  Optimal adaptive routing and traffic assignment in stochastic time-dependent networks , 2005 .