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ISPRS Int. J. Geo-Inf. 2019, 8(3), 140; https://doi.org/10.3390/ijgi8030140

Large-Scale Station-Level Crowd Flow Forecast with ST-Unet

College of Electronic Science, National University of Defense Technology, Changsha 410073, China
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Received: 24 January 2019 / Revised: 7 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Abstract

High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF. View Full-Text
Keywords: crowd flow forecast; irregular grid data; multi-output regression; deep neural network; spatio-temporal analysis crowd flow forecast; irregular grid data; multi-output regression; deep neural network; spatio-temporal analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhou, Y.; Chen, H.; Li, J.; Wu, Y.; Wu, J.; Chen, L. Large-Scale Station-Level Crowd Flow Forecast with ST-Unet. ISPRS Int. J. Geo-Inf. 2019, 8, 140.

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