Forthcoming Articles

International Journal of Shipping and Transport Logistics

International Journal of Shipping and Transport Logistics (IJSTL)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Shipping and Transport Logistics (2 papers in press)

Regular Issues

  • Artificial intelligence in ports: a bibliometric and evolutionary perspective   Order a copy of this article
    by Gulden Oner, Yelda Inanc, Gultekin Altuntas 
    Abstract: Container terminal operations are rapidly adopting artificial intelligence (AI) technologies to improve efficiency, sustainability, and automation amid ongoing digital transformation. This study conducts a comprehensive bibliometric analysis of 391 publications (1992-2024) from the Web of Science Core Collection using Biblioshiny 4.0. The findings reveal a paradigm shift toward AI-driven optimisation across key areas, including berth allocation, crane scheduling, truck fleet management, and intelligent port automation. Despite this progress, a critical gap remains: the lack of empirical validation using real operational data. This gap highlights the urgent need for cross-industry collaboration to bridge theory and practice. The study urges policymakers and port authorities to implement standardised AI frameworks, invest in workforce upskilling, and enhance digital infrastructure. Future research should focus on scalable, data-validated AI applications and on promoting longitudinal case studies and industry partnerships to ensure the effective, sustainable integration of AI technologies into port logistics.
    Keywords: artificial intelligence; container terminals; smart port applications; AI optimisation; digitalisation; maritime logistics.
    DOI: 10.1504/IJSTL.2026.10076003
     
  • Predicting carbon emission and peaking time of waterborne freight transportation in China   Order a copy of this article
    by Tingsong Wang, Mengyao Wang, Peiyue Cheng 
    Abstract: This study focuses on predicting carbon emissions from Chinas waterborne freight transport and identifying peak times. A bottom-up model based on freight turnover is developed. After comparing three models, the long short-term memory (LSTM) network is used to forecast emissions from 2021 to 2040, and the Mann-Kendall test is applied to detect peaks. Results show inland, coastal, and oceanic transport will peak in 2030, 2032, and 2029, with overall waterborne emissions peaking around 2030 and declining significantly (P<0.05). Validation confirms LSTM outperforms seasonal autoregressive integrated moving average (SARIMA) and Extreme Gradient Boosting (XGBoost). Furthermore, the study suggests that the promotion of clean energy sources, such as LNG and hydrogen, along with optimisation of energy infrastructure, could expedite the low-carbon transformation of waterborne transport. This paper offers methodological support for precise carbon emission measurement and peak time determination, providing practical reference value for Chinas achievement of its dual carbon goals.
    Keywords: waterborne freight transport; carbon emissions prediction; long short-term memory; LSTM model; Mann-Kendall trend test; peak carbon emissions time; China.
    DOI: 10.1504/IJSTL.2026.10076853
     

 

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