Abstract
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.
Abstract (translated)
交通预测在智能交通系统中扮演着至关重要的角色。物联网设备的快速发展使我们能够收集到与交通预测高度相关的各种数据，推动了多模态交通预测模型的研发。到目前为止，很少有研究关注利用多模态数据的优势来进行交通预测。在本文中，我们提出了一种新颖的时间注意力交叉模态变换器模型，称为xMTrans，具有探索两个模态（目标模态和支撑模态，例如交通拥堵）之间的时间关联的能力。我们通过使用真实世界数据对交通拥堵和出租车需求进行预测来评估我们所提出的模型。结果表明，与最先进的最近方法相比，xMTrans在长期交通预测方面具有优势。此外，我们还对xMTrans进行了全面的消融研究，以进一步分析每个模块的有效性。
URL
https://arxiv.org/abs/2405.04841
