A Review on Transit Assignment Modelling Approaches to Congested Networks: A New Perspective

Fu, Qian; Liu, Ronghui; Hess, Stephane

doi:10.1016/j.sbspro.2012.09.829

Cited by 58 publications

(32 citation statements)

References 37 publications

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“…Among these capacity phenomena, the literature review of Fu et al (2012) points out that ''the waiting time for boarding a transit vehicle at a stop/platform is an indispensable factor that needs special treatment.'' The estimation of passengers' waiting time becomes more complicated under scenarios with capacity constraints.…”

Section: Capacity Constraints In Transit Networkmentioning

confidence: 99%

Capacity constrained accessibility of high-speed rail

Shen

Zhao

2015

Transportation

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This paper proposes an enhanced measure of accessibility that explicitly considers circumstances in which the capacity of the transport infrastructure is limited. Under these circumstances, passengers may suffer longer waiting times, resulting in the delay or cancellation of trips. Without considering capacity constraints, the standard measure overestimates the accessibility contribution of transport infrastructure. We estimate the expected waiting time and the probability of forgoing trips based on the M/G B /1 type of queuing and discrete-event simulation, and formally incorporate the impacts of capacity constraints into a new measure: capacity constrained accessibility (CCA). To illustrate the differences between CCA and standard measures of accessibility, this paper estimates the accessibility change in the Beijing-Tianjin corridor due to the BeijingTianjin intercity high-speed railway (BTIHSR). We simulate and compare the CCA and standard measures in five queuing scenarios with varying demand patterns and service headway assumptions. The results show that (1) under low system loads condition, CCA is compatible with and absorbs the standard measure as a special case; (2) when demand increases and approaches capacity, CCA declines significantly; in two quasi-real scenarios, the standard measure overestimates the accessibility improvement by 14-30 % relative to the CCA; and (3) under the scenario with very high demand and an unreliable timetable, the CCA is almost reduced to the pre-BTIHSR level. Because the new CCA measure effectively incorporates the impact of capacity constraints, it is responsive to different arrival rules, service distributions, and system loads, and therefore provides a more realistic representation of accessibility change than the standard measure.

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Section: Capacity Constraints In Transit Networkmentioning

confidence: 99%

Capacity constrained accessibility of high-speed rail

Shen

Zhao

2015

Transportation

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“…For instance, when a passenger is on board, he/she has to decide to stay on board or alight and transfer at the next stop. The passenger makes these successive decisions regarding his/her perceived choice set, the availability of information, and the service reliability and irregularity (Fu et al, 2012). Nökel and Wekeck (Nökel and Wekeck, 2007;Nökel and Wekeck, 2009) discussed and compared several scenarios of information and how it may affect passengers' choice.…”

Section: Transit Assignment Modelsmentioning

confidence: 99%

“…In contrast, those passengers who have to use low-frequency services should manage their departure times to minimise the waiting time. These two assumptions appear in many existing models that determine: a) the arrival rate ofpassengers is randomly distributed, and b) travellers prefer to use the first arriving service from the attractive line set (Fu et al, 2012). In the common lines problem, the passenger's average waiting time for boarding depends on a combination of all the attractive lines' frequencies (Chriqui and Robillard, 1975).…”

Section: All or Nothing Assignmentmentioning

confidence: 99%

“…One critical aim of assignment models is to control the vehicle loadings below the capacity limits by estimating passenger loadings onto each service run, which is enormously influenced by congestion. In uncongested models, all passengers can board one of their attractive lines as the capacity limitation is not considered anymore (Fu et al, 2012). In this case, if passengers set their arrival time based on the timetable, their choice of any service would be deterministic, hence the common lines problem disappears .…”

Section: Dynamic Transit Assignmentmentioning

confidence: 99%

“…(Leurent, 2012;Sumalee et al, 2009;Hamdouch et al, 2011). These rules are: a) the on-board seated passengers will keep the seats during their journey, b) the on-board standing passengers have priority to get vacant seats over the waitto-board passengers at the stop, c) there is an equal chance of getting vacant seats for the standing on-board passengers d) the passengers get on a vehicle on the First-Come-First-Serve (FCFS) basis (Fu et al, 2012). However, Schmöcker et al (2011) assumed that all the wait-to-board passengers at the stop compete to board and to occupy available seats, rather than obeying the FCFS discipline.…”

Section: Dynamic Transit Assignmentmentioning

confidence: 99%

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A Bayesian model to improve transit assignment using smart card data

Nodehi¹

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Public transport systems are playing a significant role in providing society with cost-effective and efficient access to main services such as markets, employment, and education. Public transport planners can predict passenger loads and levels of service by applying their prior knowledge about the transit network and using transit assignment models. Static Transit Assignment (STA) is the preferred tool for strategic transport planning due to its simplicity and computational efficiency, even for large networks. Strategic transport models are often used to compare different scenarios or variants. In this context, it is important that differences between scenarios can be attributed to the scenarios themselves and not to unstable model results. Also, estimating the route choice factors, considering the fact that network cost attributes might be non-deterministic, is complicated. The individual travel history data available from automated fare collection (AFC) systems bring the opportunity of understanding the individual's travel behaviour, which is necessary to develop a transit assignment model. By combining the prior knowledge about the transit network with the AFC data, a transit assignment model can be developed. This thesis is dedicated to the application of smart card data in developing an STA model on a large-scale multimodal network, allowing for the uncertainty and the random variations of route choice parameters. To estimate the route choice parameters, a Bayesian Hierarchical model is proposed. The Bayesian model combines prior knowledge, typically in the form of a statistical distribution, with current data (such as AFC data) to derive a meaningful posterior. The inference of the number of passengers on each route, estimated from the proposed model, allows better forecasting and prediction of travellers' behaviour, and thus enables more efficient service planning for the transit network.Following a sequential approach, this thesis can be divided into six parts: (1) reviewing the literature on the existing transit assignment models and on AFC data applications in public transport networks, (2) AFC data acquisition and preparation, (3) analysing AFC data to detect passengers' route choice behaviour and defining a variety of factors affecting transit route choice, (4) developing a transit assignment model and i calibrating the model using AFC data, (5) undertaking different sensitivity analysis, and (6) evaluating the performance of the model. The six parts have interconnections and overlaps, contributing new knowledge and insights on utilizing AFC data to model and develop the public transport assignment in a large-scale multimodal network. This research has given contributions to a number of components of transit assignment modelling. The contributions of this research are sixfold. First, a new demand assignment framework, taking advantage of AFC data, is proposed to estimate the passenger flow on each segment of a multimodal transit network. Second, by taking travel time variation into consideration, the ...

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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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A Review on Transit Assignment Modelling Approaches to Congested Networks: A New Perspective

Cited by 58 publications

References 37 publications

Capacity constrained accessibility of high-speed rail

Capacity constrained accessibility of high-speed rail

A Bayesian model to improve transit assignment using smart card data

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

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