Resumen
Periodic traffic prediction and analysis is essential for urbanisation and intelligent transportation systems (ITS). However, traffic prediction is challenging due to the nonlinear flow of traffic and its interdependencies on spatiotemporal features. Traffic flow has a long-term dependence on temporal features and a short-term dependence on local and global spatial features. It is strongly influenced by external factors such as weather and points of interest. Existing models consider long-term and short-term predictions in Euclidean space. In this paper, we design an attention-based encoder?decoder with stacked layers of LSTM to analyse multiscale spatiotemporal dependencies in non-Euclidean space to forecast traffic. The attention weights are obtained adaptively and external factors are fused with the output of the decoder to evaluate region-wide traffic predictions. Extensive experiments are conducted to evaluate the performance of the proposed attention-based non-Euclidean spatiotemporal network (ANST) on real-world datasets. The proposed model has improved prediction accuracy over previous methods. The insights obtained from traffic prediction would be beneficial for daily commutation and logistics.