Envisioning AI for international cooperation in maritime transport: conceptual insights from short sea shipping and maritime spatial planning
Perra, V.-M.; Boile, M. (2026). Envisioning AI for international cooperation in maritime transport: conceptual insights from short sea shipping and maritime spatial planning. Transportation Research Interdisciplinary Perspectives 36: 101819. https://dx.doi.org/10.1016/j.trip.2025.101819
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| Author keywords |
Artificial intelligence; International cooperation; Short sea shipping; Maritime spatial planning; Key performance indicators; Composite index; Data-driven decision-making |
| Abstract |
Artificial Intelligence (AI) can significantly enhance transportation governance, particularly by enabling more effective international cooperation in data-driven decision-making. In maritime transport, AI applications can support complex planning and policy processes, such as maritime spatial planning (MSP), which governs the use of maritime space across overlapping sectors and jurisdictions. Short sea shipping (SSS), a vital mode of regional and intra-regional transport, depends heavily on coordinated planning efforts due to its interactions with other marine uses, its socio-economic role, and the need to maintain connectivity for insular economies.This study uses a national level case study of Greek SSS to identify structural, data-related, and governance limitations that impede evidence-based policy design. Key performance indicators (KPIs) and composite indices (CIs) are developed to assess connectivity, accessibility, and operational efficiency across the island and between the islands and the mainland. These empirical findings reveal fragmented data, heterogenous service patterns, and gaps in current governance frameworks, highlighting challenges that extend to regional and international coordination.Building on these insights, the paper proposes a conceptual AI framework to address the identified limitations. Machine learning can forecast SSS performance trands, while natural language processing can harmonize policy documents across jurisdictions. By linking empirical limitations with this forward-looking conceptual approach, the study demonstrates how AI can transform fragmented maritime data into interoperable, collaborative governance mechanisms that enhance MSP implementation and cross-border cooperation. |
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