Real-time prediction and detection of contacts between vessels and facilities based on AIS: A multivariate time-series classification approach

• DOI: 10.1016/j.eswa.2024.125109
• Publication Date: 2024-08-14T00:05:00Z
• Authors: Duowei Li; Yiik Diew Wong; Kim Hock Tan; Nanxi Wang; Kum Fai Yuen
• Keywords: Engineering; Vessel-to-facility collision; Maritime casualty; 610; 620

Contact casualties between vessels and fixed facilities, such as the recent Baltimore bridge collapse, occur frequently in port areas and narrow waters, resulting in significant losses in property and operational efficiency. However, manual reporting of such contacts is typically prone to errors and delays, while automatic prediction and detection of such contacts remain unresolved. To this end, this study proposes a real-time contact prediction and detection method, powered by multivariate time-series classification and Automatic Identification System (AIS) transmissions. In the off-line training procedure, the historical AIS records are processed into time-series sequences containing five dimensions (i.e., distance to ground, heading, speed over ground, course over ground, navigational status). An LSTM-based classifier is built and trained upon those sequences to differentiate patterns between contact casualties (i.e., positive-class) and normal operations (i.e., negative-class). In the on-line detection procedure, the latest transmitted AIS records are fed into the trained classifier, where the contact casualty is predicted, detected and reported in a real-time manner. A dataset encompassing worldwide AIS records of 150 reported contact casualties and 150 normal operations dated from 01/01/2023 to 31/12/2023, is constructed for method training and validation. Upon comparison, the proposed method outperforms state-of-the-art baselines by achieving the highest F1-score (0.9512), accuracy (0.9500), and AUC (0.9557). It timely predicts and detects 28 out of 30 contact casualties, while the remaining two casualties are detected with a slightly delay, demonstrating high feasibility for real-world application. ; Singapore Maritime Institute (SMI) ; This work was supported by “Safety 4.0: AI-Driven Ship Safety Management System” granted by Singapore Maritime Institute (SMI), with grant number SMI-2023-MTP-03.