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Nowadays, massive urban human mobility data are being generated from mobilephones, car navigation systems, and traffic sensors. Predicting the density andflow of the crowd or traffic at a citywide level becomes possible by using thebig data and cutting-edge AI technologies. It has been a very significantresearch topic with high social impact, which can be widely applied toemergency management, traffic regulation, and urban planning. In particular, bymeshing a large urban area to a number of fine-grained mesh-grids, citywidecrowd and traffic information in a continuous time period can be representedlike a video, where each timestamp can be seen as one video frame. Based onthis idea, a series of methods have been proposed to address video-likeprediction for citywide crowd and traffic. In this study, we publish a newaggregated human mobility dataset generated from a real-world smartphoneapplication and build a standard benchmark for such kind of video-like urbancomputing with this new dataset and the existing open datasets. We firstcomprehensively review the state-of-the-art works of literature and formulatethe density and in-out flow prediction problem, then conduct a thoroughperformance assessment for those methods. With this benchmark, we hoperesearchers can easily follow up and quickly launch a new solution on thistopic.

VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction