Assessing the Credibility of AIS-Calculated Risks in Busy Waterways: A Case Study of Hong Kong Waters

Yao Jiang, Wenyu Xu and Dong Yang ()
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Yao Jiang: College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China
Wenyu Xu: Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong
Dong Yang: Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong

Mathematics, 2025, vol. 13, issue 18, 1-17

Abstract: The increasing complexity of maritime traffic, driven by the expansion of international trade and growing shipping demand, has resulted in frequent ship collisions with significant consequences. This paper evaluates the credibility of the risk, calculated using the automatic identification system (AIS), in busy waterways and integrates AIS data with video surveillance data to comprehensively analyze the risk of ship collision. Specifically, this study utilizes the IALA Waterways Risk Assessment Program (IWRAP) tool to simulate maritime traffic flow and assess collision risk probabilities across various study areas and time periods. In addition, we analyze data from 2019 to 2022 to explore the impact of the COVID-19 pandemic on maritime traffic and find that the number of ship arrivals during the epidemic has decreased, resulting in a decrease in accident risk. We identify four traffic conflict areas in the real-world study area and point out that there are multi-directional ship interactions in these areas, but compliance with traffic rules can effectively reduce the risk of accidents. Additionally, simulations suggest that even a 13.5% increase in ocean-going vessel (OGV) traffic would raise collision risk by only 0.0247 incidents/year. To more accurately analyze the risk of waterways, we investigate the capture of dynamic information for ships in waterways by using the learning-driven detection model for real-time ship detection. These findings highlight the effectiveness of combining AIS and visual data for waterway risk assessment, offering critical insights for improving safety measures and informing policy development.

Keywords: Automatic Identification System (AIS); ship detection; ship collision; risk analysis; waterway safety (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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