Punctuality Predictions in Public Transportation: Quantifying the Effect of External Factors

Tim Meyer-Hollatz (), Nina Schwarz () and Tim Werner ()
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Tim Meyer-Hollatz: Fraunhofer Institute for Applied Information Technology FIT, Branch Business and Information Systems Engineering
Nina Schwarz: Fraunhofer Institute for Applied Information Technology FIT, Branch Business and Information Systems Engineering
Tim Werner: Fraunhofer Institute for Applied Information Technology FIT, Branch Business and Information Systems Engineering

A chapter in Solutions and Technologies for Responsible Digitalization, 2025, pp 395-410 from Springer

Abstract: Abstract Increasing availability of large-scale datasets for automatic vehicle location (AVL) in public transportation (PT) encouraged researchers to investigate data-driven punctuality prediction models (PPMs). PPMs promise to accelerate the mobility transition through more accurate prediction delays, increased customer service levels, and more efficient and forward-looking planning by mobility providers. While several PPMs show promising results for buses and long-distance trains, a comprehensive study on external factors’ effect on tram services is missing. Therefore, we implement four machine learning (ML) models to predict departure delays and elaborate on the performance increase by adding real-world weather and holiday data for three consecutive years. For our best model (XGBoost) the average MAE performance increased by 17.33% compared to the average model performance when only trained on AVL data enriched by timetable characteristics. The results provide strong evidence that adding information-bearing features improve the forecast quality of PPMs.

Keywords: Punctuality prediction; Public transportation; Machine learning; Automatic vehicle location (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-80122-8_25

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