A multi-task spatio-temporal fully convolutional model incorporating interaction patterns for traffic flow prediction

Previous traffic flow prediction studies have utilized spatio-temporal neural networks combined with the multi-task learning framework to seek complementary information for enhancing prediction performance. However, the existing methods still face two challenges: they fail to capture global interaction patterns between regions and lack consideration for inter-correlations within interaction patterns. To solve these issues, we propose a novel multi-task spatio-temporal fully convolutional model named MSTFCM. First, the model includes the interaction tensor and raster tensor as task inputs, where the interaction tensor extends the raster tensor by incorporating global interaction patterns between regions. Second, a multi-task framework combined spatio-temporal convolutional block was used to learn generalized features and interaction features. A channel spatio-temporal attention is added to adaptively adjust feature weights and capture inter-correlations. To train the MSTFCM, the uncertainty loss was designed as the learnable loss functions, which capture various flow fluctuations, to facilitate multi-task optimization. The proposed model was validated on two real-world traffic datasets collected in Xiamen, China. Experimental results showed that MSTFCM outperformed nine baselines in one-step and multi-step prediction, with slower performance degradation as predicted time intervals and steps increased. We further validated the model's effectiveness through designed variants and visualization results.