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.

翻译：交通预测在智能交通系统中扮演关键角色。物联网设备的快速发展使我们能够收集与交通预测高度相关的多种数据类型，推动了高效多模态交通预测模型的发展。然而，目前鲜有研究关注如何利用多模态数据的优势进行交通预测。本文提出一种新颖的时间注意力跨模态Transformer模型——xMTrans，专门用于长时交通预测，该模型能够探索两种模态数据之间的时间相关性：目标模态（用于预测，如交通拥堵）和支持模态（如人流）。我们利用真实世界数据集，对提出的模型在交通拥堵和出租车需求预测任务上进行了广泛评估。结果表明，xMTrans在长时交通预测方面优于当前最先进的方法。此外，我们还进行了全面的消融研究，以进一步分析xMTrans中每个模块的有效性。