DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting
This work addresses traffic forecasting for transportation systems, but it is incremental as it builds on existing graph-based methods by adding more spatial dependencies.
The paper tackled traffic speed forecasting by incorporating three spatial relationships (distance, direction, and positional) into a graph convolutional network, resulting in positive improvements for long-term forecasting in complex urban networks, with larger gains during commute hours.
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.