Title: Performance measure of dry bulk ports using shipping big data

Authors: Wenhao Peng; Xiwen Bai; Jianqiao Ren; Jie Ni; Weihong Liu

Addresses: College of Transportation Engineering, Dalian Maritime University, China ' Department of Industrial Engineering, Tsinghua University, China ' Ningbo Zhoushan Port Company Limited, Ningdong Road No. 269, Ningbo, Zhejiang, China ' Ningbo Zhoushan Port Company Limited, Ningdong Road No. 269, Ningbo, Zhejiang, China ' Ningbo Zhoushan Port Company Limited, Ningdong Road No. 269, Ningbo, Zhejiang, China

Abstract: Dry bulk port performance measure from a global macro perspective is important, as major dry bulk cargoes are closely linked to national industrial development. However, there is a lack of study systematically investigating dry bulk port performance measure using state-of-art technology, i.e., maritime big data. For a comprehensive comparison of worldwide dry bulk port performance, this study proposes a four-dimensional dry bulk port performance evaluation approach using automatic identification system (AIS) data. Dry bulk port performance is quantified in terms of connectivity, scale, efficiency and cargo flow. Using the top 15 iron ore ports as an example, the port performance of major iron ore ports is calculated and compared with AIS data in 2020. The constructed performance measure has been shown to effectively capture the key points for dry bulk ports to improve their performance, and provide insightful suggestions for both port operators and shipping companies.

Keywords: dry bulk port; port performance measure; automatic identification system; AIS; iron ore port.

DOI: 10.1504/IJSTL.2025.148489

International Journal of Shipping and Transport Logistics, 2025 Vol.21 No.2, pp.214 - 238

Received: 10 Aug 2023
Accepted: 16 Jul 2024
Published online: 08 Sep 2025 *

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