LAUSR

Accurate prediction of private car flows in urban regions is strategically vital for constructing smart cities. Private car flows are essentially reflected in the arrive-stay-leave (ASL) behaviors of car users. Specifically, to fulfill daily travel needs, private car users usually $arrive$ at specified locations, $stay$ for a certain period participating in their activities, then $leave$ to the next destination. In this paper, we strive to explore global spatiotemporal correlations of ASL behaviors to predict private car flow. Therefore, we formulate the dynamic distribution of ASL behaviors in urban regions through multiple graphs and propose the novel multigraph dense convolutional network (MGDCN) to represent spatiotemporal correlations of ASL behaviors for private car flow prediction. The proposed MGDCN is an end-to-end framework consisting of three modules: $i$) multigraph dense convolutions, in which we introduce densely connected blocks to aggregate global spatial correlations effectively, $ii$) convolutional gated recurrent units to capture sequential temporal correlations of ASL behaviors in all regions, and $iii$) attention networks for learning the stay duration correlations in each region. Experiments based on large-scale private car trajectory data and region-of-interest (ROI) datasets verify that the proposed method achieves superior performance than the baselines in all five metrics.