Spatio-temporal fusion and contrastive learning for urban flow prediction

Abstract

Urban flow prediction is critical for urban planning, management, and safety. However, owing to the inherent instability of urban flows, prediction accuracy requires the fusion of multi-view influencing factors. Current prediction methods are insensitive to periodic changes in urban flows, and rarely consider the implied spatial and temporal correlations between similar functional areas. Thus, we propose a method based on spatiotemporal fusion and contrastive learning. We construct an extraction module of spatial and temporal views based on contrastive learning in the spatial and temporal dimensions. With the temporal view extraction, we can obtain the distribution and change in the global urban flow periodicity. Using the spatial view extraction, we can obtain the implied flow variation relationship between similar regions. Therefore, our model can capture the high-level semantic features of urban flow changes from multiple views. We also design a multi-view fusion and prediction network to combine multi-view representations that impact urban flow. We experimentally evaluated our proposed approach using two real-world datasets and demonstrated state-of-the-art forecasting performance, as well as effectiveness in resource-limited environments.