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- Title
Application of Genetic Algorithms for Driverless Subway Train Energy Optimization.
- Authors
Brenna, Morris; Foiadelli, Federica; Longo, Michela
- Abstract
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.
- Subjects
DRIVERLESS cars; GENETIC algorithms; FORCE &; energy; ELECTRIC railroads; HEURISTIC algorithms
- Publication
International Journal of Vehicular Technology, 2016, p1
- ISSN
1687-5702
- Publication type
Article
- DOI
10.1155/2016/8073523