Contrastive-Learning-Based Adaptive Graph Fusion Convolution Network With Residual-Enhanced Decomposition Strategy for Traffic Flow Forecasting

Traffic flow prediction is the foundation of traffic scheduling and a major component of intelligent transportation systems (ITSs). Accurate traffic flow prediction is crucial for numerous real-world traffic applications. However, the complex and dynamic nature of spatiotemporal correlations in traffic flow data poses obstacles to this task. Another important factor hindering model improvement is the scarcity of spatio-temporal data. Meanwhile, most existing studies assume the traffic data collected by sensors in real scenarios is completely correct and trustworthy, thereby neglecting the anomalies and incorrect data. To address these issues, we propose the contrastive learning-based adaptive graph fusion convolution network with residual-enhanced decomposition strategy (CDAGF) for traffic flow forecasting, which performs a simple yet effective graph augmentation mechanism imposed only on the learned graphs and reserves the explicit graph to mitigate the data scarcity problem, and designs a negative filter to assign negative pairs based on timestamp information. Besides, CDAGF separates anomalous signals from valid traffic signals through a residual-enhanced decomposition strategy to weaken the impact of anomalies and further improve the prediction accuracy. Moreover, CDAGF generates static graph and dynamic graphs based on traffic signals as well as timestamp information and fuses the learned graphs with the explicit graph to model temporal and spatial characteristics dynamically and adaptively. The experimental results on five popular public data sets demonstrate that CDAGF achieves state-of-the-art performance with 18.29 mean absolute error (MAE) on the PEMS04 data set.