Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services

Abstract

The importance of a mobility system based on railway technology as the backbone of public transport is now widely acknowledged. Indeed, rail systems are green, high performing, smart and able to ensure a high degree of safety. Therefore, modal split should be steered towards rail transport by increasing the attractiveness of this transport mode. In this context, a key element is represented by the timetabling design phase, which must aim to guarantee an appropriate degree of robustness of rail operations in order to ensure a high degree of system reliability and increase service quality. A crucial factor in the task of timetabling entails evaluating dwell times at stations. The innovative feature of this paper is the analytical definition of dwell times as flow dependent. Our proposal is based on estimating dwell times according to the crowding level at platforms and related interaction between passengers and the rail service in terms of user behaviour when a train arrives. An application in the case of a real metro system is provided in order to show the feasibility of the proposed approach.

The Impact of Urban Transit Systems on Property Values: A Model and Some Evidences from the City of Naples

“…The proposed model and procedure were also applied to two future scenarios; the first (Scenario 1) considers the completion of Line 1 of the metro network in Naples and of Line 6, which is now entirely closed. Currently, Line 1 (a more detailed description can be found in [46]) starts from Piscinola and ends in Piazza Garibaldi (railway central station); under the full project, instead, the line will be extended to form a circle, including Capodichino Airport and the city's business district (Centro Direzionale); Line 6 is currently closed, even if part of it is completed, and is expected to be extended as far as the metro station Municipio of Line 1. Finally, we also consider the opening of Duomo B15 B9 B13 B8 C29 B17 C26 E33 E35 B14 C33 B10 B11 C24 C23 C28 D30 D35 E42 C22 C32 D27 D34 D36 B16 % to REV OMI zone station on Line 1 which is currently under construction.…”

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

“…If passengers come from another metro line, we add Transfer i;j x 1 , namely walking time in the transfer channel to formula (8).…”

“…From the network graph, some parameters are given, N Xizhimen = 5, I 0 = 1, r = 0.10. In the aims to calculate the propagation rate, we refer to formula (8); the correlation degrees a and b are given by Sect. 5.2 (a peak = 0.54, a normal = 0.57, b peak = 0.46,b normal = 0.43).…”

“…The analysis of congestion in road networks includes macroscopic [1,2], medium [3,4] and microscopic [5,6] levels. The rearrangement of demand in the network once an element fails is a particular focus of some studies [7][8][9]. Generally, there is an overlook on the dynamic properties of traffic in previous papers, such as the propagation of congestion.…”

Assumptions and simulation of passenger behaviour on rail platforms

“…The two approaches have a performance similar to the assigned travel demand since running times of convoys are assumed as constant (i.e. we neglected the increase in dwell times due to different values of travel demand whose details may be found in D'Acierno et al [33]). In fig.…”