Energy-optimal Timetable Design for Sustainable Metro Railway Networks

TL;DR

This paper introduces a real-time, data-driven linear programming model for designing energy-optimal metro train timetables that significantly reduce energy consumption and improve efficiency in large railway networks.

Contribution

The paper presents a novel, single-stage linear programming model capable of computing energy-efficient timetables in real-time, unlike previous NP-hard or multi-stage approaches.

Findings

Achieves 20.93% to 28.68% energy savings.

Computes optimal timetables in less than one second.

Validated on Shanghai Railway Network's Metro Line 8.

Abstract

We present our collaboration with Thales Canada Inc, the largest provider of communication-based train control (CBTC) systems worldwide. We study the problem of designing energy-optimal timetables in metro railway networks to minimize the effective energy consumption of the network, which corresponds to simultaneously minimizing total energy consumed by all the trains and maximizing the transfer of regenerative braking energy from suitable braking trains to accelerating trains. We propose a novel data-driven linear programming model that minimizes the total effective energy consumption in a metro railway network, capable of computing the optimal timetable in real-time, even for some of the largest CBTC systems in the world. In contrast with existing works, which are either NP-hard or involve multiple stages requiring extensive simulation, our model is a single linear programming model capable of computing the energy-optimal timetable subject to the constraints present in the railway network. Furthermore, our model can predict the total energy consumption of the network without requiring time-consuming simulations, making it suitable for widespread use in managerial settings. We apply our model to Shanghai Railway Network's Metro Line 8 -- one of the largest and busiest railway services in the world -- and empirically demonstrate that our model computes energy-optimal timetables for thousands of active trains spanning an entire service period of one day in real-time (solution time less than one second on a standard desktop), achieving energy savings between approximately 20.93% and 28.68%. Given the compelling advantages, our model is in the process of being integrated into Thales Canada Inc's industrial timetable compiler.