Coordinated railway traffic rescheduling with the Resource Conflict Graph model

Abstract

The train rescheduling problem is quite a popular topic in the railway research community. Many approaches are available to reschedule traffic in a network partition, but very few works address the coordination of these partitions. In railway systems with very dense traffic, e.g. the Swiss one, it is not always possible to partition the network such that local rescheduling algorithms can work completely independently one from another. This paper proposes a coordination approach for adjacent local rescheduling algorithms. These algorithms are based on the Resource Conflict Graph model, which enables the representation of the interlocking system at a very fine granularity. Simulations on data from the Swiss Federal Railways show the validity of this approach to improve the consistency of decisions at the common boundaries of adjacent local rescheduling algorithms.

A review of principles and methods to decompose large-scale railway scheduling problems

“…Most common is a heuristic where the penalty of a solution of the master problem is used to determine priorities to trains (subproblems); those are then, train by train, sequentially scheduled based on their priority. In Toletti et al (2020) a Lagrangian relaxation has been proposed for a geographic decomposition. Complicating constraints at the border of geographic regions are relaxed.…”

Effective train routing selection for real-time traffic management: Improved model and ACO parallel computing

“…Pellegrini et al (2019) reformulate the microscopic RECIFE-MILP model presented in Pellegrini et al (2014) by modifying the constraints and reducing the number of train scheduling variables, by means of valid inequalities that link routing and scheduling variables. Toletti et al (2020) solve the rtRTMP for railway systems by using a decomposition and coordination framework, modelled according to the resource conflict graph of Caimi et al (2011). They use a MILP commercial solver, which includes train routing variables as well, and an ad-hoc developed column generation approach to solve the local train rescheduling problems at a microscopic level.…”