A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts

doi:10.1080/23249935.2026.2616045

A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts

2026-01-01

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DOI：10.1080/23249935.2026.2616045

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This paper proposes a novel Physics-Constrained Neural Network (PCNN) approach for solving the Dynamic Origin-Destination Estimation (DODE) problem. The method combines physical constraints with deep learning, utilising traffic flow observations and network structure to accurately estimate dynamic OD matrices. This study introduces a novel spatiotemporal structure (CNN & Line-based Network Graph Aggregation) for high-accuracy estimation. By employing a Physics-Constrained Deep Learning (PCDL) and Dynamic Traffic Assignment Learner (DTAL) framework, the model integrates a machine learning surrogate into the model-driven component. This approach avoids encoding complex terms directly in the DTA process, thereby improving accuracy. Comprehensive evaluations on two networks validate the method's superior accuracy and data efficiency compared to benchmarks. Experimental results demonstrate the PCNN's robustness under various demand levels and missing data scenarios, providing a significant novel approach for traffic planning and management.

Keywords:

OD estimation

deep learning

link traffic counts

line-based graph aggregation

A physics-constrained deep learning approach for dynamic origin-destination estimation using link counts

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