  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.
