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
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:
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