Predicting and analyzing ferry transit delays using open data and machine learning

The utilization of public transport data has evolved rapidly in recent decades. Ferries, with their unique characteristics and sensitivity to weather conditions, pose significant challenges for delay prediction. Given their pivotal role in the transportation systems of numerous cities, accurately predicting ferry delays is crucial for synchronizing transit services. This paper demonstrates the value of open data for improving ferry delay predictions through machine learning, focusing on two case studies. Our approach leverages General Transit Feed Specification (GTFS) data, ridership and vessel information, and hourly weather data, combined with SHAP explainable artificial intelligence analysis to assess key delay determinants. While support vector regression and deep neural networks showed high accuracy in individual case studies, gradient boosting consistently offered the best balance between prediction accuracy and computational efficiency. Moreover, SHAP analysis reveals that operational and temporal features – such as stop sequence, trip start time, headway, and vehicle label – are the dominant drivers of delays, with weather-related factors exerting only a modest influence. • Integrated Data Approach: Combined GTFS and weather data to improve ferry delay predictions. • Machine Learning Application: Used machine learning algorithms to predict ferry delays. • Case Studies: Validated methods with case studies in Sydney and New York City. • Explainable AI: Used explainable AI to identify key factors behind ferry delays. • Multimodal Transport Efficiency: Provided insights to improve multimodal ferry transport.