United States Department of Transportation
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Routing Policy Choice Models in Stochastic Time-Dependent Networks: The Stockholm Case Study
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2018-08-14
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By Gao, Song
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TRIS Online Accession Number:01701687
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Edition:Year 25 Final Report
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Abstract:Transportation networks are frequently subject to random disruptions such as incidents and bad weather, resulting in variable and unpredictable traffic conditions. According to the 2015 Urban Mobility Scorecard, traffic congestion in the United States cost 160 billion dollars in 2014. Meanwhile, with the fast development of sensor and telecommunication technologies, real-time in- formation is increasingly available for travelers and system operators to make better decisions in such an uncertain network, which includes radios, websites, smartphone applications, in-vehicle navigation systems, and connected vehicles. A crucial component in designing and evaluating realtime travel information systems is understanding travelers’ route choice behavior in response to a wide range of traveler information situations in a network with dynamic and random traffic conditions. A traveler makes decisions based on his or her knowledge of the available alternatives and their attributes. This knowledge is periodically updated by both personal experience and exogenous information, and as a result the decisions might be revisited and revised. In other words, a traveler “adapts” to the decision environment. The time scale at which route choice adaption happens can be broadly divided into two types: day-to-day and within-day. In a day-to-day context, a traveler’s route choice today might be different from yesterday due to information collected yesterday during the trip. In a within-day context, route choice could be revised en route, e.g., taking a detour upon receiving information on a crash along the original route. This research focuses on within-day adaptive route choice, where the real-time information reflects travel conditions at or close to the decision time. The scope of our study is within-day route choice, which is arguably the most researched area of traveler response to real-time traveler information system.
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