Agent-Based Route Choice with Learning and Exchange of Information
AbstractPlanning models require consideration of travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g., willingness to switch routes with potential savings) in a differentiated market (where routes have varying tolls and levels of service). This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An agent-based route choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied to both the Sioux Falls and Chicago sketch networks. Comparisons between ARC and existing models (user equilibrium (UE) and stochastic user equilibrium (SUE)) on both networks show ARC is valid and computationally tractable. In brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of transportation planning under an integrated framework. View Full-Text
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Zhu, S.; Levinson, D. Agent-Based Route Choice with Learning and Exchange of Information. Urban Sci. 2018, 2, 58.
Zhu S, Levinson D. Agent-Based Route Choice with Learning and Exchange of Information. Urban Science. 2018; 2(3):58.Chicago/Turabian Style
Zhu, Shanjiang; Levinson, David. 2018. "Agent-Based Route Choice with Learning and Exchange of Information." Urban Sci. 2, no. 3: 58.
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