Defining Railway Traffic Conflicts and Optimising Their Resolution

Matowicki Michał, Młyńczak Jakub, Gołębiowski Piotr, Přikryl Jan

Defining Railway Traffic Conflicts and Optimising Their Resolution

Číslo: SI SCSP conference/2025
Periodikum: Transactions on Transport Sciences
DOI: 10.5507/tots.2025.010

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

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Anotace: 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.