ST-RLNet: Spatio-temporal representation learning for multi-step traffic flow prediction

Traffic flow prediction provides valuable traffic information to transportation agencies and individuals in advance. Compared to next-step prediction, multi-step prediction provides users with traffic information for a longer time horizon, allowing users to have a more comprehensive understanding of traffic conditions. So far, various methods have been proposed for multi-step traffic flow prediction. However, most of them become sub-optimal in effectively detecting the spatio-temporal correlations of traffic data. Furthermore, as the number of prediction steps increases, the input data used to predict the flow of the next step tends to deviate further from the ground truth value. This deviation leads to a rapid decrease in prediction accuracy as the number of prediction steps increases. To address these issues, in this paper, we propose a deep spatio-temporal representation learning network named ST-RLNet for multi-step traffic flow prediction. The goal is to effectively generate the traffic data representation by better capturing the complex correlations of the data. In particular, we design a network called 3D-ConvLSTMNet to effectively extract short-term and long-term spatio-temporal data correlations for the next step prediction. To solve the performance degradation problem, we propose a feedback mechanism called PS-Feedback to dynamically reconstruct temporal correlation representations of input traffic flow for each round of next-step prediction. To evaluate the performance of the ST-RLNet, we conduct extensive experiments on two real-world datasets. Experimental results show that the ST-RLNet outperforms the state-of-the-art methods in both next-step and multi-step predictions, and exhibits consistent high performance under different traffic flows.