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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F25%3A00383633" target="_blank" >RIV/68407700:21260/25:00383633 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5507/tots.2025.010" target="_blank" >https://doi.org/10.5507/tots.2025.010</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5507/tots.2025.010" target="_blank" >10.5507/tots.2025.010</a>

Result language
angličtina
Original language name
Defining Railway Traffic Conflicts and Optimising Their Resolution: A Machine Learning Perspective
Original language description
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.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
20104 - Transport engineering

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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