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.

The deployment of intelligent transportation systems (ITS) in emerging urban areas faces significant challenges due to the scarcity of historical data. Knowledge transfer across cities has shown exciting potential in addressing this issue, yet existing methods typically rely on predefined or static graphs, neglecting dynamic spatio-temporal relationships. Moreover, they handle data distribution discrepancies solely at the domain or node level, neglecting the multi-granularity heterogeneity inherent in urban data distributions. In this paper, we propose a Dynamic Graph-based Cross-city Traffic Prediction Framework (DGCTF), which introduces an adaptive graph structure learner to capture dynamic spatio-temporal relationships and complex interdependencies among cities. Besides, DGCTF addresses data discrepancy issue at multiple granularities. At the domain-level, DGCTF reduces data distribution differences across cities through a cross-domain feature alignment training method with Cross-Domain Feature Alignment Loss (CDFAL). At the node-level, DGCTF enables fine-grained adaptation via a Residual-enhanced GRU with Node-level Parameter-Matching strategy (RGRU-NodePM) by generating node-specific parameters to model node correlations across cities. Experimental results on multiple real-world datasets demonstrate that DGCTF significantly outperforms state-of-the-art methods in both short-term and long-term predictions. Notably, in several cross-city prediction tasks, DGCTF outperforms the second-best methods by up to 5.78% in MAE, 2.39% in RMSE, and 8.65% in MAPE.