Heterogeneous Modular Traffic Prediction Based on Multilayer Graph Convolutional Network

Traffic patterns in the spatiotemporal network are affected by temporal dynamics and spatial correlations. The network flows have different strengths interacting at various implicit layers, and this dynamic process needs to be further explored. Predicting future traffic based on historical data from transportation IoT has been well studied, however, most of the works focus on traffic dynamics in the homogeneous spatial or temporal structure. When the spatiotemporal graph structure turns complex, it becomes a challenge to capture the deep traffic patterns on it. In this paper, a heterogeneous modular flows graph is constructed to characterize the implied spatiotemporal correlations within the traffic data. Then, we proposed a Multilayer Graph Skip Temporal Convolution Network (MGSTCN) which extracts skip aggregated representations of node status to the modular flows graph. And an extended random walk on diverse modular graphs is used to learn the spatial dependencies. The experiments based on real traffic networks confirmed that the MGSTCN has a better performance compared to the spatiotemporal homogeneous methods.