Work

Real‐Time Multi‐Train Trajectory Optimisation and Delay Recovery Using SH‐MPC Integrated With Genetic Algorithms

Year: 2025

Type: article

Abstract: ABSTRACT This paper introduces a dynamic optimisation system that enhances the management of train delays within automatic train operation (ATO) systems, utilising an innovative integration of shrinking‐horizon model predictive control (SH‐MPC) with genetic algorithms (GA). This research focuses on optimising train trajectories to efficiently handle various delay scenarios, from temporary speed restrictions to significant halts, ensuring both energy efficiency and punctuality. The proposed SH‐MPC addresses diverse delay situations in real time, while the integration with GA overcomes the limitations of long horizon forecasting. The simulation of multiple trains on a real route demonstrates the robustness of the proposed system in adhering to scheduled timetables while reducing energy consumption.

Source: IET Intelligent Transport Systems

DOI: 10.1049/itr2.70053

Authors: Zhu Li, Ning Zhao, Clive Roberts, Lei Chen

Institution: Durham University

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: gold

Estimated APC paid: $2,790