Improved estimation of commuter waiting times using headway and commuter boarding information

Ramli, Muhamad Azfar; Jayaraman, Vasundhara; Kwek, Hyen Chee; Tan, Kian Heong; Khoon, Gary Lee Kee; Monterola, Christopher

doi:10.1016/j.physa.2017.12.022

Cited by 14 publications

(9 citation statements)

References 17 publications

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“…e waiting time at the spike bus stop is indirectly affected by T C but takes the same functional form as in equation (14). Combining the equations in this section along with equation (15) and substituting them in equation (18), it is possible to compute the average waiting time of passengers in a loop with M regular bus stops and one spike bus stop in the case of perfectly staggered buses, generalising the result in Section 2.3.…”

Section: Perfectly Staggered Busesmentioning

confidence: 99%

“…Bus bunching is generally regarded as undesirable in bus systems [1][2][3][4][5][6][7][8][9][10][11][12][13]. As explained in References [14][15][16], waiting time for passengers is reduced by a staggered bus configuration when passengers arrive continuously at bus stops. A common technique used to achieve a staggered configuration is holding [2,4,6,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Synchronising Bus Bunching to the Spikes in Service Demand Reduces Commuters’ Waiting Time

2022

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Bus bunching is ostensibly regarded as a detrimental phenomenon in bus systems. We study a bus loop with two bus stops, one regular bus stop and one spike bus stop, where bunched buses can outperform staggered buses. The spike bus stop models a bus stop connected to a train or metro service, where passengers arrive in groups at periodic intervals (spikes). We introduce the configuration of synchronised bunched buses, where bunched buses wait for the spike in demand. For a wide range of parameters, synchronised bunched buses outperform perfectly staggered buses in terms of minimising the waiting time of commuters. We present an analytical formulation of the average waiting time in the case of bunched buses, synchronised bunched buses, and perfectly staggered buses with the consideration of different passenger demands, number of buses, and bus capacity. We demonstrate the validity of our analytical results through an agent-based simulation of the bus loop system.

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Section: Perfectly Staggered Busesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronising Bus Bunching to the Spikes in Service Demand Reduces Commuters’ Waiting Time

2022

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“…e transfer time includes the walking time and the waiting time at transfer stations. Assume that all the passengers arrive at the stations randomly and are able to board the rst vehicle on the route they encounter, the average waiting time of passengers can be considered as half of the headway [27][28][29]. Let 1 be the in-vehicle travel time, 2 be the total transfer time, then they can be expressed by Equations (1) and (2), respectively.…”

Section: Model Formulationmentioning

confidence: 99%

“…e term (28) limits the capacity of sections. Constraint (29) limits the number of stations in line . Constraint (30) limits the length of line .…”

Section: Generation Of Initial Populationsmentioning

confidence: 99%

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Chai

Qing-huai

2020

Journal of Advanced Transportation

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The transit network design and frequency setting problem is related to the generation of transit routes with corresponding frequency schedule. Considering not only the influence of transfers but also the delay caused by congestion on passengers’ travel time, a multi-objective transit network design model is developed. The model aims to minimize the travel time of passengers and minimize the number of vehicles used in the network. To solve the model belongs to a NP-Hard problem and is intractable due to the high complexity and strict constraints. In order to obtain the better network schemes, a multi-population genetic algorithm is proposed based on NSGA-II framework. With the algorithm, network generation, mode choice, demand assignment, and frequency setting are all integrated to be solved. The effectiveness of the algorithm which includes the high global convergence and the applicability for the problem is verified by comparison with previous works and calculation of a real-size case. The model and algorithm can be used to provide candidates for the sustainable policy formulation of urban transit network scheme.

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“…That makes passenger less comfortable. In the literature, social sciences tried to reduce perceived or actual waiting time, or emotional and psychological costs, whereas operations research investigated waiting time reduction strategies [8][9][10][11][12], as waiting time could be considered scarce, economic resource and unavoidable [13,14]. In order to maximize time utility, some passengers allocate waiting time to activities/preoccupations.…”

Section: Introductionmentioning

confidence: 99%

On Individual Repositioning Distance along Platform during Train Waiting

Leurent

Xie

2018

Journal of Advanced Transportation

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Out of waiting times spent in rail stations on boarding platforms, some part can be reinvested by the trip-makers to optimize their positions of boarding and save on travel time for the rest of their trips. This paper provides a stochastic model, in which user's journey is decomposed into phases of, successively, walking in the access station, platform positioning, waiting for boarding, train riding, and walking in the egress station. Walking speed and target position are modeled as individual factors, and in-station distances as random variables. Service timetable is exogenous. This makes egress times and exit instants random variables that are characterized by distribution and mass probability functions under closed-forms, for both single and distributed walking speeds. Specific statistical distributions are shown to ease computation. The resulting PDF formulae make likelihood functions of the model parameters. Maximum likelihood estimation is proposed and applied to a case study of commuter rail line in Paris: journeys between stations Vincennes and La Défense along line A of the Regional Express Railways. Based on data from Automated Fare Collection and Automatic Vehicle Location systems and pertaining to an individual user, satisfactory results were obtained.

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Improved estimation of commuter waiting times using headway and commuter boarding information

Cited by 14 publications

References 17 publications

Synchronising Bus Bunching to the Spikes in Service Demand Reduces Commuters’ Waiting Time

Synchronising Bus Bunching to the Spikes in Service Demand Reduces Commuters’ Waiting Time

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

On Individual Repositioning Distance along Platform during Train Waiting

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