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原文传递 Dynamics of Urban Commuter Behavior Under Real-Time Tra£5c Ii^ormation

题名：	Dynamics of Urban Commuter Behavior Under Real-Time Tra£5c Ii^ormation
作者：	Peter Shen-Te Chen and Hani S. Mahmassani
关键词：	Intelligent Transportation Systems Technologies, Congested Real-Time Information, Urban Commuter Behavior
摘要：	The effectiveness of Intelligent Transportation Systems (ITS) technologies in enhancing the quality of life in congested urban and suburban areas critically depends on drivers’ response to these systems and to the information capabilities that they offer. In particular. Advanced Traveler Information Systems (AITS) aim to improve network flow conditions through the provision of real-time information to drivers, at the trip origin as well as en route. As decisions about the configuration and deployment of such potentially expensive technologies come under consideration, it is essential to develop the body of fundamental knowledge on driver decision-making processes under real-time information supply strategies. This report is structured as a behavioral research effort to examine the processes underlying commuter decisions on en-route diversions and d^-to-day departure time and route choices as influenced by the provision underlying real-time traffic information. This report presents a series of large-scale laboratory-like experiments in which real commuters interact with and among multiple participants in a traffic network in real-time under various information strategies through a dynamic travel simulator. This simulator considers both the supply side system performance as influenced by driver refuse to real-time traffic information as well as the demand-ride driver behavior as influenced by real-time traffic information based on system performance. Its "engine'’ is a traffic flow simulator and ATTS information generator. By actually simulating traffic conditions in response to the supplied commuter decisions, the simulator provides stimuli to the participants that are always consistent with physically realistic traffic behavior, and with their previous actions. The data collected from these experiments form the observational basis for the development and calibration of Poisson event count models of user compliance and satisfaction behavior as well as multinomial probit models of dynamic departure time and route switching decisions. By estimating these models, substantive conclusions regarding the factors influencing the commuter behavior in response to various ATTS systems are obtained, addressing key fundamental issues critical to the further deployment of ITS technologies. Moreover, these models may be used in simulation-assignment to evaluate network performance under real-time traveler information and traffic control strategies.
报告类型：	科技报告