de la Paz Guala, Ricardo; Cortés, Cristián E; Heydecker, Benjamin G; Rey, Pablo; (2022) A Markovian approach for stochastic dynamic traffic assignment. Elsevier BV

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Abstract

In dynamic traffic assignment (DTA) models, it seems relevant to consider the uncertainty inherent to motorist route choices. Particularly, choices on realistic transport networks are mostly made using motorists’ perceived costs of all routes from their origins to their destinations. We present an approach to address stochastic DTA based on nested cost operators, where motorists choose according to the perceived costs of the remaining trip, namely, from current position to destination. We integrate the Markovian traffic equilibrium by Baillon and Cominetti with the DTA formulation by Addison and Heydecker to generate the Markovian dynamic traffic assignment (MDTA) model, an arc-based stochastic DTA model that accommodates overlapping routes respecting costs correlation and First In First Out rule. We present a solution method for discretized time periods, computational results on an illustrative network, including sensitivity analyses of the parameters, and comparisons with a previous suitable stochastic DTA model from the literature.

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