New Paper in the Journal of Public TransportationPredicting and analyzing ferry transit delays using open data and machine learning

25 June 2025, by Julia Bachale

Photo: Kevin Hackert/Flickr

A new paper with the title "Predicting and analyzing ferry transit delays using open data and machine learning" has been published in the Journal of Public Transportation. In addition to Stefan Voß and Abtin Nourmohammadzadeh of IWI, our former guest researcher Malek Sarhani of Al Akhawayn University in Ifrane as well as Mohammed El Amrani of Mohammed V University in Rabat, Morocco are the authors. Please click here for the whole article:

Abstract:

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.