Estimation of Dynamic Origin–Destination Matrices Using Linear Assignment Matrix Approximations

Tomer Toledo · Tanya Kolechkina

80 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Transportation Planning and Optimization Traffic control and management Traffic Prediction and Management Techniques

Abstract

This paper presents a general solution scheme for the problem of offline estimation of dynamic origin-destination (OD) demand matrices using traffic counts on some of the network links and historical demand information. The proposed method uses linear approximations of the assignment matrix, which maps the OD demand to link traffic counts. Several iterative algorithms that are based on this scheme are developed. The various algorithms are implemented in a tool that uses the mesoscopic traffic simulation model Mezzo to conduct network loadings. A case study network in Stockholm, Sweden, is used to test the proposed algorithms and to compare their performance with current state-of-the-art methods. The results demonstrate the applicability of the proposed methodology to efficiently obtain dynamic OD demand estimates for large and complex networks and that, computationally, this methodology outperforms existing methods.

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References

1998Implementation of the simultaneous perturbation algorithm for stochastic optimization 2001Transportation Systems Engineering: Theory and Methods 1993Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts 1995Heuristic algorithms for the bilevel origin-destination matrix estimation problem 2002Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows 2000Estimation of origin–destination matrices from link flows on uncongested networks 2001Fixed Point Approaches to the Estimation of O/D Matrices Using Traffic Counts on Congested Networks 2001A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows

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2015c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation 2023Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach 2019PC–SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration 2021A practical approach to assignment-free Dynamic Origin–Destination Matrix Estimation problem

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