Predicting Urban Region Heat via Learning Arrive-Stay-Leave Behaviors of Private Cars

Zhu Xiao, Hao Li, Hongbo Jiang, You Li, Mamoun Alazab, Yongdong Zhu, Schahram Dustdar

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)


Urban region heat refers to the extent of which people congregate in various regions when they travel to and stay in a specified place. Predicting urban region heat facilitates broad applications ranging from location-based services to intelligent transportation management. The region heat is essentially characterized by the 'arrive-stay-leave (ASL)' behaviors, while it is a challenging task to well capture the spatial-temporal evolution of region heat since the following issues remain: i) ASL behaviors of private cars is usually heterogeneous resulting in a hierarchical distribution of region heat. ii) Urban region heat contains complex spatial-temporal correlations hidden in ASL behaviors and how to collaboratively integrate them is challenging. To address these challenges, we propose a Hierarchical Spatial-Temporal Network (HierSTNet) to forecast urban region heat, which contains two representations, namely, grid region from micro perspective and node region from macro perspective. For the grids, three-dimension spatial and temporal convolutional network (3D-STCNN) is proposed to model multi-scale properties in temporal dimension of ASL behaviors. For the nodes, multi-head graph attention networks are utilized to model the periodicity and spatial heterogeneity among macro region. Hierarchical structures are designed for multi-view modeling spatial-temporal distribution of ASL behaviors, by which they capture small-scale features in micro regions and embeds the global representation into graph propagation. Finally, we design an interaction decoder layer to integrate the external factors and aggregate spatial-temporal information across hierarchical structures. Extensive experiments based on real-world private car trajectory dataset demonstrate the superiority and effectiveness of proposed framework.

Original languageEnglish
Pages (from-to)10843 - 10856
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
Early online date2023
Publication statusPublished - 1 Oct 2023

Bibliographical note

Funding Information:
This work was supported in part by the NSFC under Grant U20A20181 and Grant 62272152, in part by the National Key Research and Development Program of China under Grant 2022YFE0137700, in part by the Humanities and Social Sciences Foundation of Ministry of Education under Grant 21YJCZH183, in part by the Science and Technology Innovation Program of Hunan Province under Grant 2021RC4023, in part by the Key Research and Development Program of Hunan Province under Grant 2021WK2001 and Grant 2022GK2020, in part by the Hunan Natural Science Foundation of China under Grant 2022JJ30171, in part by the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy [Shenzhen (SZ)] under Grant GML-KF- 22-22 and Grant GML-KF-22-23, in part by the Shenzhen Science and Technology Program under Grant JCYJ20220530160408019, in part by the CAAI-Huawei MindSpore Open Fund, and in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515011915.

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