Travel Decision Making Under Uncertainty and Road Traffic Behavior: The Multifold Role of Ambiguity Attitude
Access status:
Open Access
Type
Working PaperAbstract
To aggregate commuters’ mode choices to traffic behavior in the presence of travel time uncertainty, we develop a dynamic traffic simulation in terms of an agent-based model, which consists of two sub-models, the mode choice model and the traffic flow simulation model. The modeling ...
See moreTo aggregate commuters’ mode choices to traffic behavior in the presence of travel time uncertainty, we develop a dynamic traffic simulation in terms of an agent-based model, which consists of two sub-models, the mode choice model and the traffic flow simulation model. The modeling framework accommodates the interplay between the two models and their co-evolution over time. We embed an extended list of empirical parameters including ambiguity/risk attitudes and heterogeneity , and time-money trade-offs within a rank-dependent and source-dependent utility framework to imitate commuters’ daily mode choice behaviors. The improved behavioral realism at the micro-level results in an improved understanding of traffic flow in terms of modal split and average speed at equilibrium, compared to a conventional model which assumes risk neutrality and ambiguity neutrality. A novel finding is that ambiguity seeking, a typical behavior in the loss domain but largely ignored in the transport literature, acts as an important driver that shifts commuters from cars to public transport.
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See moreTo aggregate commuters’ mode choices to traffic behavior in the presence of travel time uncertainty, we develop a dynamic traffic simulation in terms of an agent-based model, which consists of two sub-models, the mode choice model and the traffic flow simulation model. The modeling framework accommodates the interplay between the two models and their co-evolution over time. We embed an extended list of empirical parameters including ambiguity/risk attitudes and heterogeneity , and time-money trade-offs within a rank-dependent and source-dependent utility framework to imitate commuters’ daily mode choice behaviors. The improved behavioral realism at the micro-level results in an improved understanding of traffic flow in terms of modal split and average speed at equilibrium, compared to a conventional model which assumes risk neutrality and ambiguity neutrality. A novel finding is that ambiguity seeking, a typical behavior in the loss domain but largely ignored in the transport literature, acts as an important driver that shifts commuters from cars to public transport.
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Date
2023-04-18Licence
Copyright All Rights ReservedFaculty/School
The University of Sydney Business SchoolDepartment, Discipline or Centre
Institute of Transport and Logistic Studies (ITLS)Share