Pre-trip and en-route multi-modal travel decisions considering habitual travel times under unexpected incident scenarios

Modeling impacts of incidents and their associated management strategy have been important topics from the standpoint of both Intelligent Transportation Systems (ITS) planning and operations. Modeling questions of interest are often addressed through the application of simulation models. However, it is often known that existing models' limited realism hinder the validity and accuracy of modeling outcomes. This research focuses on addressing several aspects of real-time en-route diversion due to an incident scenario. Contribution to literature is the congestion responsive diversion rule aimed at establishing a plausible behavioral mechanism that has been omitted in related literatures. In a case study, various diversion rules due to congestion reaction or disseminated information are evaluated comprehensively to provide detailed insights to these rules.

[1]  Isam Kaysi,et al.  DESIGN OF AN INTEGRATED DATA COLLECTION PROGRAM TO SUPPORT MODELING OF USER RESPONSE TO ATIS SERVICES , 1994 .

[2]  Hani S. Mahmassani,et al.  Dynamic Network Traffic Assignment and Simulation Methodology for Advanced System Management Applications , 2001 .

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

[4]  Mohamed Abdel-Aty,et al.  Modeling Incident-Related Routing Decisions by Using a Nested Logit Structure , 1998 .

[5]  Zhiyong Guo,et al.  Day-to-Day Evolution of Network Flows Under Route-Choice Dynamics in Commuter Decisions , 2004 .

[6]  Will Recker,et al.  Using Microscopic Simulation to Evaluate Potential Intelligent Transportation System Strategies Under Nonrecurrent Congestion , 2004 .

[7]  Yi-Chang Chiu,et al.  A Computationally Efficient and Temporally Scalable Dynamic Traffic Simulation and Assignment System – Dynamic Urban Systems in Transportation ( DynusT ) , 2010 .

[8]  Haris N. Koutsopoulos,et al.  Models for route choice behavior in the presence of information using concepts from fuzzy set theory and approximate reasoning , 1993 .

[9]  A.J. Khattak,et al.  Effect of traffic reports on commuters' route and departure time changes , 1991, Vehicle Navigation and Information Systems Conference, 1991.

[10]  Hani S. Mahmassani,et al.  COMPARATIVE ASSESSMENT OF ORIGIN-BASED AND EN ROUTE REAL-TIME INFORMATION UNDER ALTERNATIVE USER BEHAVIOR RULES , 1991 .

[11]  D. McFadden.  Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments , 1977 .

[12]  Kuilin Zhang,et al.  Dynamic micro-assignment modeling approach for integrated multimodal urban corridor management , 2008 .

[13]  Haris N. Koutsopoulos,et al.  Network State Estimation and Prediction for Real-Time Traffic Management , 2001 .

[14]  Hani S. Mahmassani,et al.  Role of Congestion and Information in Trip-Makers’ Dynamic Decision Processes: Experimental Investigation , 1999 .

[15]  Kay W. Axhausen,et al.  The potential of information provision in a simulated road transport network with non-recurrent congestion , 1995 .

[16]  Hani S. Mahmassani,et al.  DYNAMIC INTERACTIVE SIMULATOR FOR STUDYING COMMUTER BEHAVIOR UNDER REAL-TIME TRAFFIC INFORMATION SUPPLY STRATEGIES , 1993 .

[17]  Hani S. Mahmassani,et al.  Day-to-day evolution of network flows under real-time information and reactive signal control , 1997 .

[18]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[19]  Ryuichi Kitamura,et al.  Impact Of ATIS On Drivers' Decisions And Route Choice: A Literature Review , 1993 .