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MIKTA maritime research gaps: Data-driven machine learning approach for sustainable collaboration

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  • Lee, Yong-Jae

Abstract

The maritime industry is a cornerstone of global trade but faces significant sustainability challenges. International collaboration is crucial to address these issues, particularly for middle-income nations like MIKTA countries. This study employs a data analytics and machine learning approach to identify potential areas for collaborative research in sustainable maritime technology within MIKTA. By utilizing Latent Dirichlet Allocation (LDA) topic modeling, we categorized research papers into sub-fields and identified potential collaborations. Network and self-organizing map (SOM) analyses further refined these findings, revealing three priority areas with high collaboration potential but limited research: (1) developing a Sustainable Maritime Economy Realization Model (Indonesia-Korea), (2) creating an environmentally friendly and efficient port operation system (Mexico-Australia), and (3) establishing a Sustainable Management System for port workforce safety and health (Indonesia-Turkey). These insights can inform research and policy agendas, accelerating the development and adoption of sustainable maritime technologies within MIKTA and contributing to global maritime sustainability.

Suggested Citation

  • Lee, Yong-Jae, 2025. "MIKTA maritime research gaps: Data-driven machine learning approach for sustainable collaboration," Journal of Transport Geography, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:jotrge:v:128:y:2025:i:c:s0966692325002662
    DOI: 10.1016/j.jtrangeo.2025.104375
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