Crowding on public transport for Île-de-france

Kroes, Eric; Kouwenhoven, Marco; Debrincat, Laurence; Pauget, Nicolas

doi:10.1787/9789282107683-6-en

Cited by 14 publications

(2 citation statements)

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“…SP research has the inherent limitation that there might be a discrepancy between the behavior stated by respondents and their realized behavior, the latter being used in revealed preference (RP) research. Therefore, there is a risk of potential bias when estimating crowding multipliers based on SP research, as suggested by studies where selected RP data was used to validate SP results (Kroes et al, 2014;Batarce et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

New Methods, Reflections and Application Domains in Transport Appraisal

Mouter¹

2021

Advances in Transport Policy and Planning

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Section: Methodsmentioning

confidence: 99%

New Methods, Reflections and Application Domains in Transport Appraisal

Mouter¹

2021

Advances in Transport Policy and Planning

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“…The proposed model contains two sets of parameters: (i) crowding coefficients incorporated in the route-choice model, and (ii) learning coefficients included in the dayto-day function. The former can be estimated using stated preferences extracted from choice experiments (30,31) or revealed preferences inferred from smart card data records (32,33). The learning coefficients are conventionally estimated using repeated-experiment games (28).…”

Section: Methodsmentioning

confidence: 99%

Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks

Cats

West²

2020

Transportation Research Record: Journal of the Transportation R

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The distribution of passenger demand over the transit network is forecasted using transit assignment models which conventionally assume that passenger loads satisfy network equilibrium conditions. The approach taken in this study is to model transit path choice as a within-day dynamic process influenced by network state variation and real-time information. The iterative network loading process leading to steady-state conditions is performed by means of day-to-day learning implemented in an agent-based simulation model. We explicitly account for adaptation and learning in relation to service uncertainty, on-board crowding and information provision in the context of congested transit networks. This study thus combines the underlying assignment principles that govern transit assignment models and the disaggregate demand modeling enabled by agent-based simulation modeling. The model is applied to a toy network for illustration purposes, followed by a demonstration for the rapid transit network of Stockholm, Sweden. A full-scale application of the proposed model shows the day-to-day travel time and crowding development for different levels of network saturation and when deploying different levels of information availability.

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Modelling public transport on‐board congestion: comparing schedule‐based and agent‐based assignment approaches and their implications

Cats

Hartl

2016

J of Advced Transportation

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Summary Transit systems are subject to congestion that influences system performance and level of service. The evaluation of measures to relieve congestion requires models that can capture their network effects and passengers' adaptation. In particular, on‐board congestion leads to an increase of crowding discomfort and denied boarding and a decrease in service reliability. This study performs a systematic comparison of alternative approaches to modelling on‐board congestion in transit networks. In particular, the congestion‐related functionalities of a schedule‐based model and an agent‐based transit assignment model are investigated, by comparing VISUM and BusMezzo, respectively. The theoretical background, modelling principles and implementation details of the alternative models are examined and demonstrated by testing various operational scenarios for an example network. The results suggest that differences in modelling passenger arrival process, choice‐set generation and route choice model yield systematically different passenger loads. The schedule‐based model is insensitive to a uniform increase in demand or decrease in capacity when caused by either vehicle capacity or service frequency reduction. In contrast, nominal travel times increase in the agent‐based model as demand increases or capacity decreases. The marginal increase in travel time increases as the network becomes more saturated. Whilst none of the existing models capture the full range of congestion effects and related behavioural responses, existing models can support different planning decisions. Copyright © 2016 John Wiley & Sons, Ltd.

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Crowding on public transport for Île-de-france

Cited by 14 publications

References 0 publications

New Methods, Reflections and Application Domains in Transport Appraisal

New Methods, Reflections and Application Domains in Transport Appraisal

Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks

Modelling public transport on‐board congestion: comparing schedule‐based and agent‐based assignment approaches and their implications

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