Table of Contents
International Journal of Vehicular Technology
Volume 2016, Article ID 8073523, 14 pages
Research Article

Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

Politecnico di Milano, Department of Energy, Via La Masa 34, 20156 Milano, Italy

Received 30 July 2015; Revised 23 December 2015; Accepted 5 January 2016

Academic Editor: Sanghyun Ahn

Copyright © 2016 Morris Brenna et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


After an introduction on the basic aspects of electric railway transports, focusing mainly on driverless subways and their related automation systems (ATC, ATP, and ATO), a technique for energy optimization of the train movement through their control using genetic algorithms will be presented. Genetic algorithms are a heuristic search and iterative stochastic method used in computing to find exact or approximate solutions to optimization problems. This optimization process has been calculated and tested on a real subway line in Milan through the implementation of a dedicated Matlab code. The so-defined algorithm provides the optimization of the trains movement through a coast control table created by the use of a genetic algorithm that minimizes the energy consumption and the train scheduled time. The obtained results suggest that the method is promising in minimizing the energy consumption of the electric trains.