PT Journal
AU Michal, M
   Jakub, M
   Piotr, G
   Jan, P
TI Defining Railway Traffic Conflicts and Optimising Their Resolution: A Machine Learning Perspective
SO Transactions on Transport Sciences
PY 2025
BP 44
EP 48
VL 16
IS 3
DI 10.5507/tots.2025.010
DE Machine Learning; identification and classification of conflicts; conflict resolution; railway traffic management; global optimisation;
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.
ER