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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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Basic Information:
China Classification Code:P228.4;U653.93
Citation Information:
[1]YANG Hao,FENG Xuejun,XIAO Fu ,et al.Research on identifying port service areas using truck GPS data[J].Modern Transportation and Metallurgical Materials,2026,6(03):8-18.
Fund Information:
国家重点研发计划(2021YFB2600203)
2025-11-07
2025
2025-12-31
2026-02-25
2026
1
2026-05-15
2026-05-15