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

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 10; pp. 1 - 14
• Main Authors: Xiao, Zhu, Li, Hao, Jiang, Hongbo, Li, You, Alazab, Mamoun, Zhu, Yongdong, Dustdar, Schahram
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: arrive-stay-leave behaviors; Automobiles; Behavioral sciences; Correlation; Data models; Graphical representations; Heating systems; Heterogeneity; hierarchical spatial-temporal network; Location based services; private cars; Public transportation; Temporal distribution; Trajectory; trajectory data; Transportation management; Transportation networks; Urban region heat
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3276704

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.