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Design of a Network and Travel Simulator: A Tool Box for the Analysis of Route Choice Behavior in the Presence of Information


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Suggested Citation:
Vaughn, Kenneth M., Prasuna D. Reddy, Ryuichi Kitamura, Paul P. Jovanis, Mohamed A. Abdel-Aty (1994) Design of a Network and Travel Simulator: A Tool Box for the Analysis of Route Choice Behavior in the Presence of Information. Institute of Transportation Studies, University of California, Davis, Presentation Series UCD-ITS-RP-94-24

Computing in Civil Engineering: Proceedings, First Congress held in conjunction with A/E/C Systems. Vol. 2

The application of advanced technology and its potential to alleviate a host of traffic related problems has spawned numerous research programs into the investigation of how advances in information technologies may be utilized to significantly alter traveler behavior. If real-time, accurate information on the characteristics of the travel environment can be provided to travelers prior to departure and while enroute, will behavior be altered in such a way as to improve the overall characteristics of the travel environment? In order to accurately model the macrolevel effects of Advanced Traveler Information Systems (ATIS), the micro-level effects of these systems on individual driver behavior must be analyzed and understood. The use of computer simulation and modeling is being utilized as a data collection tool to study advanced traveler devices to supplement real operational systems and as a cost effective alternative to field studies. Researchers at the University of California at Davis are utilizing PC-based computer simulation to study the effects of information on individuals' route choice behavior and learning. Previous research utilized a simplistic binary route choice simulation to collect sequential data on subjects decisions. Building on the efforts from this previous simulation, a new set of experiments utilizes an expanded traffic network and provides various levels of information content to the subjects. This framework allows for investigation of both pre-trip and enroute decision behavior and captures the effect of different levels of information on route choice and diversion. In this series of experiments futher investion of drivers learning and adaptive processes are also explored.