A Detection Method for Maritime Traffic Accidents Based on AIS Communication Volume

The data-driven approach for traffic accident detection plays a crucial role in the rapid rescue and reduction of losses in maritime accidents. To achieve automatic detection of maritime accidents without autonomous reporting, a method based on Automatic Identification System (AIS) communication volume is proposed. Normally, sudden maritime accidents disrupt the normal navigation patterns of vessels, leading to sharp changes in AIS communication volume within a short time due to changes in vessel movement states during the accidents. To extract the inherent laws of AIS communication volume during the evolution of maritime accidents and reduce noise interference to highlight the abrupt features of detection indicators, the AIS communication volume-to-total vessel count ratio is introduced as an indicator for maritime accident detection. To ensure timely detection, a sliding window model is used to segment detection indicator data and set update time intervals. Furthermore, a maritime accident detection model based on Kalman filtering is developed for short-term prediction of detection indicators. To ensure the accuracy of detection results, a cloud model is employed for rapid division of detection model threshold ranges. Validations and simulations are conducted using AIS data from the Yangtze River Wuhan section to verify the AIS communication volume-based maritime accident detection method. Results show that the proposed detection model based on Kalman filtering achieves the highest hit rate and lowest false alarm rate in the shortest time, namely 97.25% and 0.42%, respectively, compared to models employing standard normal deviation and multi-scale linear fitting algorithms. In further simulated experiments involving three different accident scenarios, the proposed AIS communication volume-based maritime accident detection method successfully detects accidents within 5 minutes.