Multi-view dynamic graph convolution neural network for traffic flow prediction

Abstract

The rapid urbanization and continuous improvement of road traffic equipment result in massive daily production of traffic data. These data contain the long-term evolution of traffic flow and dynamic changes in the traffic road network. Due to the complex topology of the traffic road network, traffic flow prediction is challenging as it contains complex, multi-periodic patterns, and is often affected by sudden events. In this paper, we propose a Multi-View Dynamic Graph Convolution Network (MVDGCN) that captures different levels of spatial–temporal dependencies to predict traffic flow. Firstly, we use the coupling graph convolution network to learn the relationship matrix among stations dynamically, capturing the spatial dependencies at different levels in the traffic network. Secondly, we establish three encoder–decoders, representing hourly, daily, and weekly views, to extract the evolution law of traffic flow from three different time periods. Finally, we use the dynamic fusion module to merge the spatial–temporal dependencies extracted from the multi-view encoder–decoders. We conducted experiments on two real datasets, NYCTaxi and NYCBike, and found that our proposed MVDGCN model outperformed the best baseline, improving the RMSE, MAE, PCC, and MAPE by 12.9%, 6.2%, 0.8%, and 6.5% respectively on the NYCBike dataset and 9.2%, 4.2%, 4.6%, and 3.0% respectively on the NYCTaxi dataset. These results show that the proposed MVDGCN model performs better than state-of-the-art algorithms.