Methods for quantitative assessment of passenger flow influence on train dwell time in dense traffic areas - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Transportation Research Part C: Emerging Technologies Année : 2019

Methods for quantitative assessment of passenger flow influence on train dwell time in dense traffic areas

Résumé

Railway operations in dense traffic areas are very sensitive even to small disturbances, and thus require careful planning and real-time management. Dwell times in stations are in particular subject to a high variability and are hard to predict; this is mostly due to the interactions between passengers and the railway system during the dwelling process. This paper presents a data-driven approach for assessing the influence of the numbers of alighting, boarding and on board passengers on the dwell time. We propose to split the dwell time into a deterministic component depending on the passenger flow, called the Minimum Dwell Time, and a random component. A method for estimating the minimum dwell time is provided. Based on the knowledge of this value, observations can be classified according to the main determinant of dwell time, namely timetable constraints or passenger exchange. The latter observations are used for estimating the conditional distribution of dwell time given passenger flows. Numerical experiments are carried out on stations located inside the dense traffic area of Paris suburban network. The obtained results indicate that the presented method can be used for a variety of applications, such as capacity assessment or stochastic simulation
Fichier principal
Vignette du fichier
doc00031805.pdf (3.49 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02958118 , version 1 (11-06-2021)

Identifiants

Citer

Selim Cornet, Christine Buisson, François Ramond, Paul Bouvarel, Joaquin Rodriguez. Methods for quantitative assessment of passenger flow influence on train dwell time in dense traffic areas. Transportation Research Part C: Emerging Technologies, 2019, 106, pp344-359. ⟨10.1016/j.trc.2019.05.008⟩. ⟨hal-02958118⟩
52 Consultations
75 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More