xMTrans: Temporal Attentive Cross-Modality Fusion Transformer for Long-Term Traffic Prediction
This addresses traffic prediction for intelligent transportation systems by leveraging multi-modal data, though it appears incremental as it builds on existing multi-modal and transformer approaches.
The paper tackles long-term traffic prediction by introducing xMTrans, a model that fuses multi-modal data (e.g., traffic congestion and people flow) using temporal attention, achieving superior performance against state-of-the-art methods in experiments on real-world datasets.
Traffic predictions play a crucial role in intelligent transportation systems. The rapid development of IoT devices allows us to collect different kinds of data with high correlations to traffic predictions, fostering the development of efficient multi-modal traffic prediction models. Until now, there are few studies focusing on utilizing advantages of multi-modal data for traffic predictions. In this paper, we introduce a novel temporal attentive cross-modality transformer model for long-term traffic predictions, namely xMTrans, with capability of exploring the temporal correlations between the data of two modalities: one target modality (for prediction, e.g., traffic congestion) and one support modality (e.g., people flow). We conducted extensive experiments to evaluate our proposed model on traffic congestion and taxi demand predictions using real-world datasets. The results showed the superiority of xMTrans against recent state-of-the-art methods on long-term traffic predictions. In addition, we also conducted a comprehensive ablation study to further analyze the effectiveness of each module in xMTrans.