Treffer: Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways

Title:
Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways
Authors:
DÜNDAR, Selim, SAHIN, Ismail
Source:
Transportation research. Part C, Emerging technologies. 27:1-15
Publisher Information:
Kidlington: Elsevier, 2013.
Publication Year:
2013
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Transportation, Transports, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Transports terrestres, transports aeriens, transports maritimes, constructions navales, Ground, air and sea transportation, marine construction, Economie, gestion et planification des transports, Transportation planning, management and economics, Transports et trafic ferroviaires, Railway transportation and traffic, Algorithme génétique, Genetic algorithm, Algoritmo genético, Codage binaire, Binary coding, Codificación binaria, Etude méthode, Method study, Estudio método, Méthode calcul, Computing method, Método cálculo, Ordonnancement, Scheduling, Reglamento, Problème livraison, Dispatching problem, Problema reparto, Recommandation, Recommendation, Recomendación, Répartiteur, Dispatcher, Repartidor, Réseau neuronal, Neural network, Red neuronal, Résolution conflit, Conflict resolution, Resolución conflicto, Simulation numérique, Numerical simulation, Simulación numérica, Train, Tren, Transport ferroviaire, Rail transportation, Transporte ferroviaro, Voie ferrée, Railway track, Vía férrea, Artificial neural networks, Binary encoding, Genetic algorithms, Train dispatching, Train re-scheduling
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Okan University, Tuzla Campus, Engineering and Architecture Faculty, Department of Civil Engineering, 34959 Akfirat/İstanbul, Turkey
Yıldız Technical University, Davutpaşa Campus, Department of Civil Engineering, Transportation Division, 34210 Esenler/İstanbul, Turkey
ISSN:
0968-090X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27055320
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Building. Public works. Transport. Civil engineering
Accession Number:
edscal.27055320
Database:
PASCAL Archive

Weitere Informationen

Train re-scheduling problems are popular among researchers who have interest in the railway planning and operations fields. Deviations from normal operation may cause inter-train conflicts which have to be detected and timely resolved. Except for very few applications, these tasks are usually performed by train dispatchers. Due to the complexity of re-scheduling problems, dispatchers utilize some simplifying rules to resolve conflicts and implement their decisions accordingly. From the system effectiveness and efficiency point of view, their decisions should be supported with appropriate tools because their immediate decisions may cause considerable train delays in future interferences. Such a decision support tool should be able to predict overall implications of the alternative solutions. Genetic algorithms (GAs) for conflict resolutions were developed and evaluated against the dispatchers' and the exact solutions. The comparison measures are the computation time and total (weighted) delay due to conflict resolutions. For benchmarking purposes, artificial neural networks (ANNs) were developed to mimic the decision behavior of train dispatchers so as to reproduce their conflict resolutions. The ANN was trained and tested with data extracted from conflict resolutions in actual train operations in Turkish State Railways. The GA developed was able to find the optimal solutions for small sized problems in short times, and to reduce total delay times by around half in comparison to the ANN (i.e., train dispatchers).