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Improving the Realism and Carbon-Reduction Performance of Weather Routing Using AIS-Derived Maritime Traffic Networks
- 언어ENG
- URLhttps://db.koreascholar.com/Article/Detail/442966
pp.110-120
Efficient yet realistic ship routing is critical for reducing fuel consumption and greenhouse-gas emissions. However, conventional weather-routing algorithms often produce mathematically optimal routes that conflict with the paths mariners use. This study presents a hybrid approach that constrains physics-based weather routing within an AISderived maritime traffic network (MTN) built from one year of global Automatic Identification System data. The MTN represents common sea lanes as a graph of approximately 10,956 waypoints (nodes) and 17,561 directed edges. Using this network, an optimal low-emission route is computed via graph search and then compared against both a traditional unconstrained route and an advanced weather-routing model (VISIR-2). In a May transitionseason case (Busan–Singapore voyage), the AIS-constrained route reduced fuel consumption and CO₂ emissions by about 1.9% relative to the fastest feasible route, while closely following real traffic corridors (over 90% overlap with actual 2024 AIS tracks). While this 1.9% saving does not reach the high-end potential of an unconstrained, state-of-the-art model like VISIR-2 (which can demonstrate double-digit savings in certain conditions), it is achieved with an increase in transit time of ~6.5 h (≈3.2%). This represents a crucial trade-off, prioritizing operational realism and adherence to real-world traffic corridors over maximum theoretical efficiency.
1. Introduction
2. Background and Related Work
3. Methodology and Case Study Setup
3.1 AIS-Derived Graph Construction:
3.2 Environmental Modelling and Route Optimization:
3.3 Benchmark Routing Methods:
4. Results and Analysis
4.1 Route Comparison
4.2 Transit Time and Fuel Consumption
4.3 Realism and Compliance Analysis
5. Discussion and Future Work
5.1 Limitations of the Current Model
5.2 Future Work and Extensions
6. Conclusion
Acknowledgements
References
