Abstract
This paper aims to analyse the interpretability of public transport delay predictions using post-hoc model interpretability methods. Using Gradient Boosted Machines and the Shapley Additive Explanations (SHAP) method, we identify the factors with the greatest impact on predicted delays. The results were presented as a list of the most important factors contributing to the delay, along with a spatial analysis of the results for individual public transport lines and the entire urban area. The tests were performed on monthly data from streamed localisations of public transportation means in the City of Warsaw, amounting to more than 66 million records. The proposed solution increases the transparency of predictive models and can support public transport operators in making operational and planning decisions.
Paper Type
Poster
DOI
10.62036/ISD.2026.65
Explanation of Public Transport Delay Predictions
This paper aims to analyse the interpretability of public transport delay predictions using post-hoc model interpretability methods. Using Gradient Boosted Machines and the Shapley Additive Explanations (SHAP) method, we identify the factors with the greatest impact on predicted delays. The results were presented as a list of the most important factors contributing to the delay, along with a spatial analysis of the results for individual public transport lines and the entire urban area. The tests were performed on monthly data from streamed localisations of public transportation means in the City of Warsaw, amounting to more than 66 million records. The proposed solution increases the transparency of predictive models and can support public transport operators in making operational and planning decisions.
Recommended Citation
Szewczak, M. & Luckner, M.(2026). Explanation of Public Transport Delay Predictions. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.65