Dynamic Origin-Destination Flow Imputation Using Feature-Based Transfer Learning

Real-time and full-sample vehicle origin-destination (OD) information is essential for traffic management and control in urban road network. However, the low coverage of automatic vehicle identification (AVI) detection devices leads to difficulty in estimating OD. As an emerging traffic data, the trajectories of connected vehicles (CVs) can effectively provide information on their origin and destination. To this end, this paper presents a framework of an autoencoder network utilizing feature transfer to estimate urban dynamic OD based on the characteristics of two data sources. Specifically, a generative adversarial network is introduced to learn high-dimensional feature that is domain-invariant in two data domains. In addition, a pre-training fine-tuning approach is proposed to transfer knowledge pretrained from CV data to the limited AVI observation for OD imputation. Finally, the model was subjected to a real-world road network test. The results showed that for all OD flows the relative error was 11.23 vehicles/30 minutes, which outperformed baseline models, including popular neural networks and existing estimation models for multi-source data fusion. Furthermore, the model's robustness to external factors, such as observation conditions and data quality, was examined. The results demonstrated that the model consistently delivers satisfactory estimation performance across a diverse range of conditions.