Reinforced Spatio-Temporal Attentive Graph Neural Networks for Traffic Forecasting

The advances in the Internet of Things (IoT) and increased availability of the road sensors allow for fine-grained traffic forecasting, which is of particular importance toward building an intelligent transportation system. In the literature, recent efforts have applied various deep learning methods for traffic forecasting, e.g., leveraging graph convolutional networks (GCNs) for spatial dependency modeling, and utilizing recurrent neural networks (RNNs) for capturing temporal dynamics. However, most of the existing approaches assume that spatial correlations are static and temporal correlations have only sequential dependencies and do not consider temporal periodicity of traffic across multiple time steps. The real challenge lies in using the dynamic spatiotemporal correlations while also considering the influence of the nontraffic-related factors, such as time-of-day and weekday-or-weekend in the learning architectures. We propose a novel framework titled “reinforced spatial–temporal attention graph (RSTAG) neural networks” for traffic prediction. Our method captures dynamic spatial correlations through diffusion network graphs, while temporal dependencies are represented through the sequence-to-sequence model with an attention mechanism. In addition, we utilize the policy gradient to update the model parameters while largely alleviating the exposure bias issue that exists in previous traffic prediction models. We conduct extensive experiments on two large-scale traffic data sets collected from the road sensor networks in Los Angles and Bay Area of California. The results demonstrate that our method significantly outperforms the state-of-the-art baselines.