Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2019
Abstract
Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition
Discipline
Databases and Information Systems
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Mobile Computing
Volume
19
Issue
12
First Page
2804
Last Page
2817
ISSN
1536-1233
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Embargo Period
3-28-2021
Citation
WANG, Xu; ZHOU, Zimu; ZHAO, Yi; ZHANG, Xinglin; XING, Kai; XIAO, Fu; YANG, Zheng; and LIU, Yunhao.
Improving urban crowd flow prediction on flexible region partition. (2019). IEEE Transactions on Mobile Computing. 19, (12), 2804-2817.
Available at: https://ink.library.smu.edu.sg/sis_research/5886
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TMC.2019.2934461