Adding flexibility to the timetable in real-time railway traffic management

Adding flexibility to the timetable in real-time railway traffic management

ZHOU, Johnny (TU Delft Civil Engineering & Geosciences; TU Delft Transport and Planning)

Goverde, R.M.P. (graduation committee)
Wang, Z. (mentor)
Correia, Gonçalo (graduation committee)
Philipsen, Edith (graduation committee)

Delft University of Technology

Civil Engineering

2023-11-30

With the strong backing and advocacy from the EU for railway transport, it is crucial to focus on innovative and efficient technologies to maintain service quality. In daily train operations, traffic uncertainties lead to perturbations, which may result in two types of track conflicts: disturbances and disruptions.

In the current research, we aim to add and optimize timetable flexibility in traffic disturbance management for railway transport. A new definition is proposed for timetable flexibility. Timetable flexibility is defined as the ability of a timetable to be easily modified to withstand small disturbances and absorb delays, as well as to offer a larger solution space in the application of dispatching measures (retiming, reordering, rerouting) to solve larger disturbances without changing the given (re)scheduled timetable.
In order to minimize the deviation of the rescheduling plan from the rigid timetable, and maximize the timetable flexibility, the conflict resolution problem is modeled using an Alternative Graph (AG)-based Mixed Integer Linear Programming (MILP) model.

In order to investigate the impacts of different parameter inputs on timetable flexibility, a case study is conducted on a part of the Dutch railway network. To investigate the influencing factors of timetable flexibility, one illustrative application and two sensitivity analyses are conducted. Based on the results, practical implications on train dispatchers and signalers, as well as on railway passengers are concluded.

Timetable flexibility
Railway traffic management
Alternative Graph (AG)
Operational planning
Mixed Integer Linear Programming (MILP)

http://resolver.tudelft.nl/uuid:4bb9385f-b936-4117-8322-4d7c96eb71a3

Student theses

master thesis

© 2023 Johnny ZHOU