Methods for quantitative assessment of passenger flow influence on train dwell time in dense traffic areas. (September 2019)

• Record Type: Journal Article
• Authors: Cornet, Sélim; Buisson, Christine; Ramond, François; Bouvarel, Paul; Rodriguez, Joaquin
• Is Part Of: Transportation research. Volume 106(2019)
• Page Start: 345
• Page End: 359
• Publication Date: 2019-09
• Subjects: Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.05.008
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Passenger flows between train and platform can be described by a single variable. Minimum dwell time is obtained as a function of this variable. Actual dwell time is the deterministic minimum dwell time plus random components. Conditional distribution of dwell time given the passenger flow is computed. Abstract: 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.