Dynamic Origin-Destination Matrix Estimation in Urban Traffic Networks

• URL: http://arxiv.org/abs/2202.00099v1
• Date: Mon, 31 Jan 2022 21:33:46 GMT
• Title: Dynamic Origin-Destination Matrix Estimation in Urban Traffic Networks
• Authors: Nicklas Sindlev Andersen, Marco Chiarandini, Kristian Debrabant
• Abstract summary: We model the problem as a bi-level optimization problem. In the inner level, given a tentative travel demand, we solve a dynamic traffic assignment problem to decide the routing of the users between their origins and destinations. In the outer level, we adjust the number of trips and their origins and destinations, aiming at minimizing the discrepancy between the counters generated in the inner level and the given vehicle counts measured by sensors in the traffic network.
• Score: 0.05735035463793007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the counters of vehicles that traverse the roads of a traffic network, we aim at reconstructing the travel demand that generated them expressed in terms of the number of origin-destination trips made by users. We model the problem as a bi-level optimization problem. In the inner level, given a tentative travel demand, we solve a dynamic traffic assignment problem to decide the routing of the users between their origins and destinations. In the outer level, we adjust the number of trips and their origins and destinations, aiming at minimizing the discrepancy between the consequent counters generated in the inner level and the given vehicle counts measured by sensors in the traffic network. We solve the dynamic traffic assignment problem employing a mesoscopic model implemented by the traffic simulator SUMO. Thus, the outer problem becomes an optimization problem that minimizes a black-box objective function determined by the results of the simulation, which is a costly computation. We study different approaches to the outer level problem categorized as gradient-based and derivative-free approaches. Among the gradient-based approaches, we study an assignment matrix-based approach and an assignment matrix-free approach that uses the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Among the derivative-free approaches, we study machine learning algorithms to learn a model of the simulator that can then be used as a surrogated objective function in the optimization problem. We compare these approaches computationally on an artificial network. The gradient-based approaches perform the best in terms of archived solution quality and computational requirements, while the results obtained by the machine learning approach are currently less satisfactory but provide an interesting avenue of future research.

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