Smart routing has emerged as a critical enabler of sustainable shipping, addressing the growing demand for energy-efficient, safe, and adaptive vessel navigation in both maritime and inland waterborne transport. This review examines the current landscape of trajectory optimization approaches by analyzing selected peer-reviewed studies and categorizing them into six thematic areas: AI/ML-based prediction, optimization and path planning algorithms, data-driven methods using AIS and GIS, weather routing and environmental modeling, digital platforms and decision support systems, and hybrid or rule-based frameworks for autonomous navigation. The analysis highlights recent advances in deep learning for trajectory forecasting, multi-objective and heuristic optimization techniques, and the use of real-time environmental data in routing decisions. Supplemental review using Scopus-based topic mapping confirms the centrality of integrated digital strategies, high-performance computing, and physics-informed modeling in emerging research. Despite notable progress, the field remains fragmented, with limited real-time integration, underexplored regulatory alignment, and a lack of explainable AI applications. The review concludes by outlining future directions, including the development of hybrid and interpretable optimization frameworks, and expanding research tailored to inland navigation with its distinct operational challenges. These insights aim to support the design of next-generation navigation systems that are robust, intelligent, and environmentally compliant.
Kalinichenko et al. (Sun,) studied this question.
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