Work

Practical predictive model for evaluating performance and delay propagation in multi-track railways during disruptions

Year: 2025

Type: article

Abstract: Numerous studies have explored modelling approaches for managing disruption in rail networks, yet the examples of practical applications remain somewhat limited. This paper introduces a real-time, practical predictive modelling approach to simulate rail network operations during disruptions. In the United Kingdom, the rail network is highly congested, with the train services closely interconnected. As a result, disruptions can easily propagate the effects of delays across the entire network. This approach aims to simulate the propagation of delay, analyse network interdependencies, and predict rail network performance during disruptions. The results indicate a good match between the in-field data and the results of the simulation, illustrating the potential of the model to produce accurate estimations. This model has the potential to serve as a real-time advisory tool for managing disruptions in a highly congested rail network, swiftly producing reliable estimates of the outcomes for different service alterations before introduction.

Source: Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit

DOI: 10.1177/09544097251344652

Authors: Kang Rui Tan, Rasa Remenyte-Prescott, Darren Prescott, Julia Culley

Institutions: University of Nottingham, Network Rail

Language: English

Topic: Railway Systems and Energy Efficiency

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Faculty of Engineering, University of Nottingham