TFSG: A Self-Supervised Guided Graph Learning Method for Traffic Flow Prediction

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules, etc., distance bias is nontrivial to affect traffic flow in reality. Besides, the distance-based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multihead self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified.