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为精准识别港口群内部公路集疏运范围，基于集疏运货车GPS数据，提出一种识别港口公路运输模式下服务范围的方法，以支撑港口群内部竞争与合作策略的决策。在对海量集疏运货车GPS数据清洗和OD(Origin-Destination)信息提取的基础上，基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)空间聚类算法识别出核心节点；依据节点-港口的运输频次确定OD选择概率，采用Delaunay三角剖分方法界定港口在公路运输模式下的精确服务范围。福厦泉港口群的案例结果显示：公路运输模式下3个港口的服务范围存在不同特征，腹地重叠和孤岛现象明显；竞争最激烈的区域出现在较深入的内陆区域，且服务范围的重叠现象主要出现在港口各自服务范围的末端区域。研究发现：优化运输网络与差异化定位是提升港口竞争力的关键。
Abstract：To accurately delineate the road-based collection and distribution areas within port clusters, a data-driven method is developed using GPS trajectory data from collection and distribution trucks, thereby supporting decision-making for competition and cooperation strategies among ports. After preprocessing large-scale GPS data from collection and distribution trucks and extracting origin –destination(OD) pairs, core logistics nodes are identified through the DBSCAN(Density-Based Spatial Clustering of Applications with Noise)spatial clustering algorithm. Subsequently, OD-based port choice probabilities are determined based on the transport frequency between nodes and ports. Delaunay triangulation is employed to precisely delineate the port service areas under the road transport mode. A case analysis of the Fuzhou – Xiamen – Quanzhou port cluster reveals heterogeneous spatial structures, characterized by overlapping hinterlands and "island" service zones. The most intense competition occurs in inland regions, while overlaps are concentrated at the peripheral boundaries of each port's service area.
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基本信息:
中图分类号:P228.4;U653.93
引用信息:
[1]杨豪,封学军,肖富,等.基于货车GPS数据的港口服务范围识别研究[J].现代交通与冶金材料,2026,6(03):8-18.
基金信息:
国家重点研发计划(2021YFB2600203)
2026-05-15
2026-05-15