Assessing train timetable efficiency in a Mass Transit context using a data-based simulation method
| Autor: | François Ramond, Selim Cornet, Joaquin Rodriguez, Paul Bouvarel, Christine Buisson |
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| Přispěvatelé: | SNCF Réseau, Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel, SNCF Innovation & Recherche, Évaluation des Systèmes de Transports Automatisés et de leur Sécurité (COSYS-ESTAS ), Université de Lille-Université Gustave Eiffel |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
TRANSPORT EN COMMUN
Operations research Computer science 0211 other engineering and technologies TRAIN TIME TABLE ESTIMATION Context (language use) 02 engineering and technology TRAIN TABLE HORAIRE Robustness (computer science) ROBUSTNESS 11. Sustainability 0502 economics and business Stochastic simulation DENSITE DU TRAFIC Transit (satellite) 050210 logistics & transportation MODELE STOCHASTIQUE 021103 operations research TRAITEMENT DES DONNEES Stochastic process Quality of service 05 social sciences MODELISATION [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation SIMULATION Probability distribution Train ZONE PERIURBAINE |
| Zdroj: | IEEE ITSC 2020, 23rd IEEE International Conference on Intelligent Transportation Systems IEEE ITSC 2020, 23rd IEEE International Conference on Intelligent Transportation Systems, Sep 2020, Rhodes, France. pp2404-2409 ITSC |
| Popis: | IEEE ITSC 2020, 23rd IEEE International Conference on Intelligent Transportation Systems, Rhodes, GRECE, 20-/09/2020 - 23/09/2020; In order to provide a satisfying quality of service to passengers, companies operating suburban trains seek to implement timetables that perform well despite the occurrence of random disturbances. However, due to some specificities of Mass Transit, the standard approaches for robust train timetabling do not apply in that context. In this paper, we present a data-based stochastic simulation method for assessing the efficiency of train timetables for dense traffic areas. We describe models for the random variabilities that occur daily on such networks, and use them in a stochastic simulation algorithm. Results are presented on a saturated line of Paris suburban network. |
| Databáze: | OpenAIRE |
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