文献类型：期刊文献

英文题名：Commercial OD flow prediction with multi-feature spatial representation learning network

作者：Peng, Xia[1];Niu, Yueyan[2];Yang, Ming[3];Liu, Zeyu[3];Fu, Mingjian[3];Wang, Zhenghong[3];Wang, Yi[4];Huang, Zhou[4]

第一作者：彭霞

通讯作者：Wang, ZH[1]

机构：[1]Beijing Union Univ, Tourism Coll, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Appl Arts & Sci, Beijing, Peoples R China;[3]Fuzhou Univ, Coll Comp & Data Sci, 2 Xueyuan Rd,Shangjie Town, Fuzhou 350108, Fujian, Peoples R China;[4]Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China

第一机构：北京联合大学旅游学院

通讯机构：[1]corresponding author), Fuzhou Univ, Coll Comp & Data Sci, 2 Xueyuan Rd,Shangjie Town, Fuzhou 350108, Fujian, Peoples R China.

年份：2026

卷号：45

外文期刊名：TRAVEL BEHAVIOUR AND SOCIETY

收录：;WOS:【SSCI(收录号:WOS:001778063700001)】；

基金：Funding source This study was supported by the State Key Laboratory of Intelligent Transportation System under Project 2025-A003 and the National Natural Science Foundation of China (Grant No. 424B2013 and 42471272) .

语种：英文

外文关键词：Commercial OD flow prediction; Graph Convolutional Network; Multi-source data fusion; Spatial interaction modeling; Supply-demand matching

摘要：As core nodes of economic activity and consumption, urban commercial districts generate visitor flow dynamics that directly impact the operational efficiency of commercial facilities and the effectiveness of urban spatial planning. Origin-Destination (OD) flow prediction has been widely employed to accurately uncover urban spatial mobility patterns from residential areas to commercial districts, serving as a pivotal tool for elucidating the alignment between commercial attractiveness and residential demand. However, existing models often overemphasize geospatial features while neglecting the intrinsic socio-economic attributes of both commercial districts and residential communities. Consequently, these approaches fail to adequately characterize the supply-demand alignment, as they limit their scope primarily to spatial proximity and overlook the consumption similarity inherent in economic activities. To address this gap, this study proposes the Multi-Feature Spatial Representation Learning Network (MFSRNet), integrating commercial attractiveness, residential purchasing power, and demographic composition. Furthermore, unlike traditional functional similarity graphs that rely on static land-use attributes, we incorporate a Dual Graph Convolutional Network specifically designed to capture the latent economic homophily. This network concurrently learns spatial dependencies based on geographic proximity and consumption similarity derived from purchasing power and housing prices, thereby enabling a sophisticated simulation of individual commercial mobility decisions. Extensive experiments conducted on real-world data from Beijing demonstrate that MFSRNet consistently outperforms baseline models by at least 13.83%. Overall, this study presents a competitive model with enhanced performance and strong generalizability for commercial OD flow prediction. It offers data-driven decision-making support to enterprises for market positioning, business optimization, and precision marketing, while aiding urban planning authorities in optimizing the allocation of commercial resources so as to promote balanced regional economic development.