Speech Recognition-Based Analysis of Vessel Traffic Service (VTS) Communications for Estimating Advisory Timing

Vessel Traffic Service systems play a critical role in maritime safety by providing timely advisories to vessels in congested waterways. However, the optimal timing for VTS operator interventions has remained largely unstudied, relying primarily on subjective operator experience rather than empirical evidence. This study presents the first large-scale empirical analysis of VTS operator intervention timing using automated speech recognition technology applied to actual maritime communication data. VHF radio communications were collected from five major VTS centers in Korea over nine months, comprising 171,175 communication files with a total duration of 334.2 h. The recorded communications were transcribed using the Whisper speech-to-text model and processed through natural language processing techniques to extract encounter situations and advisory distances. A tokenization and keyword framework was developed to handle Maritime English and local-language communications, normalize textual numerical expressions, and facilitate cross-site analysis. Results reveal that VTS operator intervention timing varies by encounter type. In head-on and crossing encounters, advisories are provided at distances, with mean values of 3.1 nm and 2.8 nm, respectively. These quantitative benchmarks provide an empirical foundation for developing standardized VTS operational guidelines and decision support systems, ultimately enhancing maritime safety and operational consistency across jurisdictions.