Next Article in Journal
Information Exchange between GIS and Geospatial ITS Databases Based on a Generic Model
Next Article in Special Issue
Hot Spot Analysis versus Cluster and Outlier Analysis: An Enquiry into the Grouping of Rural Accommodation in Extremadura (Spain)
Previous Article in Journal
Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods
Previous Article in Special Issue
Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
Open AccessArticle

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

College of Electronic Science, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(3), 140; https://doi.org/10.3390/ijgi8030140
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop