Uncovering and modeling the hierarchical organization of urban heavy truck flows

Yitao Yang, Bin Jia, Xiao-Yong Yan, Danyue Zhi, Dongdong Song, Yan Chen, Michiel de Bok, Lóránt A. Tavasszy and Ziyou Gao

Transportation Research Part E: Logistics and Transportation Review, 2023, vol. 179, issue C

Abstract: Knowledge of the hierarchical organization of urban heavy truck flows is important for understanding the structure of urban freight system and underlying interactions dynamics, providing insights to assess and develop freight policies. The complexity and dynamic nature of urban freight system pose significant challenges in comprehensively capturing structured arrangement of heavy truck movements. In this paper, we uncover the hierarchical organization of urban heavy truck flows by using complex network theory. We use large-scale heavy truck GPS data and urban freight location point-of-interest (POI) data to construct urban heavy truck mobility networks, and detect their community structure. The empirical results suggest different sets of locations are closely linked to each other to form multiple clusters. By integrating the categories of locations, we reveal the cluster-specific industry concentration and industry-specific location roles, informing evidence-based policy formulation. To capture the interaction dynamics of locations, we develop a spatial network growth model that considers the spatial agglomeration of industrial clusters and interaction pattern of locations. The model provides a mathematical tool to simulate the formation process of real-world networks for logistics planning and management.

Keywords: Complex networks; Heavy truck flows; Community structure; Interaction dynamics (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.tre.2023.103318

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