Transactions on Transport Sciences 2025, 16(3):44-48 | DOI: 10.5507/tots.2025.010

Defining Railway Traffic Conflicts and Optimising Their Resolution: A Machine Learning Perspective

Matowicki Michała, Młyńczak Jakubb, Gołębiowski Piotrc, Přikryl Jana
a. Czech Technical University in Prague, Faculty of Transportation Sciences, Konviktska 20, 110 00 Prague
b. Silesian University of Technology, Faculty of Transport and Aviation Engineering, Katowice, Poland
c. Warsaw University of Technology, Faculty of Transport, Koszykowa 75, 00-662 Warsaw, Poland

This paper reports on the initial phase of research into automated traffic conflict resolution for suburban railway operations. It defines railway traffic conflicts, categorising types such as catch-up, crossing, and proximity, and establishes optimisation criteria focused on punctuality, efficiency, safety, and passenger satisfaction. Promising machine learning approaches are reviewed, including supervised learning for conflict prediction, reinforcement learning for adaptive resolution, and unsupervised methods for identifying conflict-prone scenarios. The study concludes by proposing a simulation framework for empirical evaluation, providing a foundation for AI-driven advancements in railway traffic management.

Keywords: Machine Learning; identification and classification of conflicts; conflict resolution; railway traffic management; global optimisation;

Received: May 6, 2025; Revised: May 6, 2025; Accepted: May 6, 2025; Published: May 28, 2025  Show citation

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Michał, M., Jakub, M., Piotr, G., & Jan, P. (2025). Defining Railway Traffic Conflicts and Optimising Their Resolution: A Machine Learning Perspective. Transactions on Transport Sciences, 16(SI SCSP conference), 44-48. doi: 10.5507/tots.2025.010
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