A data-driven optimization of large-scale dry port location using the hybrid approach of data mining and complex network theory. (February 2020)

• Record Type: Journal Article
• Authors: Van Nguyen, Truong; Zhang, Jie; Zhou, Li; Meng, Meng; He, Yong
• Is Part Of: Transportation research. Volume 134(2020)
• Publication Date: 2020-02
• Subjects: Transportation -- Data mining -- Large scale optimization -- Dry ports -- Complex network theory; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2019.11.010
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: Use data mining and complex network for large-scale dry port location optimization. Optimal locations are derived from microeconomic and macroeconomic perspectives. Chinese dry ports have strong community structure in the Belt and Road Initiatives. 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.