交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (3): 60-71.DOI: 10.16097/j.cnki.1009-6744.2026.03.006

• 青年基金项目成果 • 上一篇    下一篇

货运社区视角下城市货运介观功能定位与形成机理解析

袁泉*1 ，潘瑞煦1 ，梁星宇1 ，李卓雅2 ，杨超1   

  1. 1. 同济大学，道路与交通工程教育部重点实验室，上海201804；2.长安大学，运输工程学院，西安710064
  • 收稿日期:2025-11-25 修回日期:2026-01-14 接受日期:2026-02-04 出版日期:2026-06-25 发布日期:2026-06-22
  • 作者简介:袁泉（1989—），男，江西吉安人，副教授。
  • 基金资助:
    国家自然科学基金青年科学基金(52302394)。

Meso-scale Functional Positioning and Formation Mechanism of Urban Freight from Perspective of Freight Communities

YUAN Quan*1, PAN Ruixu1, LIANG Xingyu1, LI Zhuoya2,YANG Chao1   

  1. 1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; 2. School of Transportation Engineering, Chang'an University, Xi'an 710064, China
  • Received:2025-11-25 Revised:2026-01-14 Accepted:2026-02-04 Online:2026-06-25 Published:2026-06-22
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(52302394)。

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摘要： 为揭示城市货运活动的空间集聚规律与区域功能差异，系统解析介观货运功能定位的核心影响因素及差异化作用机制，本文提出“货运社区”作为刻画城市货运空间组织的介观基本单元，以上海市为研究区域开展实证研究。基于上海市重型货车轨迹数据构建货运流动性网络，采用Louvain算法识别出79个结构清晰和内部联系紧密的货运社区，划分模块度达0.89；通过构建涵盖货运规模、流量结构、作业特征与专业化程度的多维指标体系，运用k-means++聚类算法将货运社区划分为核心门户枢纽、规模化制造基地、外围多元服务区、中心城区消费区及专业封闭产业区5类；进一步采用XGBoost（ExtremeGradientBoosting）模型结合SHAP（SHapleyAdditive exPlanations）值解析社区功能定位的影响机制，明确港口距离、铁路货运站距离、高速公路距离、快速路距离及人口密度为核心驱动变量（累计贡献度达70%~80%），且各变量对不同功能社区的影响呈现显著非线性特征与阈值效应。研究明晰了货运社区的功能定位与空间组织逻辑，可为城市货运空间的精准治理、基础设施优化配置及差异化管控策略制定提供理论依据与实践指导。

关键词: 城市交通, 货运功能定位, 社区发现算法, 城市货运, 货运流动性

Abstract: This study aims to reveal the spatial agglomeration patterns and regional functional differences of urban freight activities. It also intends to conduct an in-depth analysis of the core influencing factors and differentiated action mechanisms of meso-scale freight functional positioning. To achieve these goals, this study proposes "freight communities" as the basic meso scale unit for characterizing urban freight spatial organization, and carries out an empirical study in Shanghai. Based on the heavy duty truck trajectory data in Shanghai, a freight mobility network is constructed. The Louvain algorithm is adopted to identify 79 freight communities with clear structures and close internal connections, achieving a modularity of 0.89. A multi-dimensional indicator system is established, covering freight scale, flow structure, operation characteristics, and specialization level. The k means++ clustering algorithm is used to classify the freight communities into five types: core gateway hubs, large-scale manufacturing bases, peripheral multi-service areas, central urban consumer areas, and specialized enclosed industrial zones. Furthermore, the Extreme Gradient Boosting (XGBoost) model combined with SHapley Additive exPlanations (SHAP) values is used to analyze the influence mechanism of community functional positioning. Five core driving variables are identified: distance to ports, distance to railway freight stations, distance to expressways, distance to urban expressways, and population density, with a cumulative contribution rate of 70%~80%. It is found that the impacts of these variables on different functional communities exhibit significant non-linear characteristics and threshold effects. This study clarifies the functional positioning and spatial organization logic of freight communities. It can provide a theoretical basis and practical guidance for the precise governance of urban freight space, the optimal allocation of infrastructure, and the formulation of differentiated management and control strategies.

Key words: urban transportation, freight functional positioning, community detection algorithm, urban freight, freight mobility

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