xMTrans: Temporal Attentive Cross-Modality Fusion Transformer for Long-Term Traffic Prediction

arXiv:2405.04841v1 Announce Type: new
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 …

abstract arxiv correlations cs.ai cs.lg data development devices fusion intelligent intelligent transportation iot long-term modal multi-modal prediction prediction models predictions role studies systems temporal traffic transformer transportation type