A data-driven optimization of large-scale dry port location using the hybrid approach of data mining and complex network theory

Truong Van Nguyen, Jie Zhang, Li Zhou, Meng Meng and Yong He

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 134, issue C

Abstract: The paper proposes a two-stage approach that combines data mining and complex network theory to optimize the locations and service areas of dry ports in a large-scale inland transportation system. In the first stage, candidate locations of dry ports are weighted based on their eigenvector centrality in the complex network of association rules mined from a large amount of international transaction data. In the second phrase, dry port locations and their service areas are optimized using the gravity-based community structure. The method is validated in a real case study which optimizes a large-scale dry port network in Mainland China in the context of the Belt and Road Initiatives (BRI). As a result, optimal dry port locations include key transportation hubs that closely reflect the real BRI development plan, hence, the proposed approach is validated.

Keywords: Transportation; Data mining; Large scale optimization; Dry ports; Complex network theory (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.tre.2019.11.010

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