Dynamic OD Estimation Using Spatio-Temporal Hybrid Graph Convolutional Network with Asynchronous Multi-Source Data

Reliable estimation of dynamic Origin-Destination (OD) matrices is essential for urban traffic management but remains a critical challenge. Emerging data sources, such as Connected Vehicle (CV) trajectories and Automatic Vehicle Identification (AVI) records, provide valuable insights for dynamic OD estimation. However, most existing approaches overlook the asynchronous nature of real-world data collection, limiting their applicability in complex urban environments. This study proposes a novel k-Nearest Neighbors Dynamic Adaptive Hybrid Graph Encoder (k-DAHGE) model that leverages the daily periodicity of travel demand to integrate asynchronous CV and AVI data for dynamic OD estimation. Firstly, we develop a spatio-temporal dependency modeling framework that addresses temporal inconsistencies among multi-source data by constructing dynamic OD relation graphs from historical CV-OD matrices. Then, we introduce a Dynamic Adaptive Hybrid Graph Encoder with learnable mixing parameters that dynamically fuses different graph convolution networks for multi-scale feature extraction. Experiments on a real-world urban road network demonstrate that our approach achieves a mean absolute error of 10.82 veh/30min for dynamic OD estimation, significantly outperforming popular neural networks and existing models. Further analyses under varying AVI coverage levels confirm the robustness and generalizability of the proposed approach even with limited AVI detection.