RT Journal Article
SR Electronic
A1 Michał, Matowicki
A1 Jakub, Młyńczak
A1 Piotr, Gołębiowski
A1 Jan, Přikryl
T1 Defining Railway Traffic Conflicts and Optimising Their Resolution: A Machine Learning Perspective
JF Transactions on Transport Sciences
YR 2025
VO 16
IS 3
SP 44
OP 48
DO 10.5507/tots.2025.010
UL https://tots.upol.cz/artkey/tot-202503-0007.php
AB 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.