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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Transactions on Transport Sciences

ISSN

1802-9876

e-ISSN

1802-9876

Svazek periodika

16

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

5

Strana od-do

44-48

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105008273728