DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting

Traffic speed forecasting is one of the core problems in transportation systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focused on modeling the spatial dependencies using the distance only. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the fundamental building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into deep neural networks. We evaluate the proposed model with two large-scale real-world datasets, and find positive improvements for long-term forecasting in highly complex urban networks. The improvement can be larger for commute hours, but it can be also limited for short-term forecasting.

Automated summary

This study focuses on traffic speed forecasting in transportation systems, using graph convolutional networks to incorporate spatial information from the road network. Unlike previous approaches, this paper considers two important spatial dependencies, direction and positional relationship, in addition to distance. By constructing basic graph elements and integrating these spatial relationships into deep neural networks, the proposed DDP-GCN model shows positive improvements for long-term forecasting in complex urban networks, especially during peak hours.