Equilibrium traffic assignment for large scale transit networks

Nguyen, Sang; Pallottino, Stefano

doi:10.1016/0377-2217(88)90327-x

Cited by 324 publications

(155 citation statements)

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“…On the other hand, we prove that in the context of commuting passengers, who know by previous experience the number of carrier passages they must let go before being able to board, expected waiting times and passenger distribution among attractive lines may be calculated by extending the formulas presented by Nguyen and Pallottino (1988) and Spiess and Florian (1989). Results obtained with our model will be compared with those detailed in Table II (uncongested model) and Table III (effective frequency model), and it will be shown that the FIFO queue model penalises the congested line, in terms of passengers' distribution, more than the uncongested and the effective frequency model, while the total expected waiting time increases.…”

Section: Background Researchmentioning

confidence: 80%

“…Some authors (Spiess 1983, Spiess 1984, Nguyen and Pallottino 1988, Spiess and Florian 1989 prove that, when there is this uncertainty, rather than the single shortest itinerary between origin and destination, route choice can be modelled as the selection of the optimal strategy (Spiess and Florian 1989), and graphically represented as the shortest hyperpath on an oriented hypergraph 1 modelling the transit network (Nguyen and Pallottino 1988, Wu et al 1994.…”

Section: Background Researchmentioning

confidence: 99%

“…The optimal strategy is selected pre-trip and, starting from the origin, involves the iterative sequence of: walking to a public transport stop or to the destination, selecting the attractive (Nguyen and Pallottino 1988) lines to board and, for each of them, the stop where to alight. Once travelling towards the destination, if two or more attractive lines are available at a stop, the best option is to board the first approaching (Spiess 1983(Spiess , 1984.…”

Section: Background Researchmentioning

confidence: 99%

“…Recalling the definition of an attractive set given in (Nguyen and Pallottino 1988), we can write: () if it has a shorter remaining travel time than any other attractive one. Therefore, it is possible to solve the combinatorial problem described above through a greedy approach (Spiess and Florian 1989, Nguyen and Pallottino 1988, Chriqui and Robillard 1975.…”

Section: Attractive Setmentioning

confidence: 99%

“…In this context, if no service schedule is published or services are highly frequent and/or unreliable, users have no explicit knowledge about carriers' arrival time at transit stops. So, if there are two or more competing lines, so-called common lines (Chriqui and Robillard 1975), they may face the question if it is more convenient to board the first approaching line or wait for another, they consider more convenient.Some authors (Spiess 1983, Spiess 1984, Nguyen and Pallottino 1988, Spiess and Florian 1989 prove that, when there is this uncertainty, rather than the single shortest itinerary between origin and destination, route choice can be modelled as the selection of the optimal strategy (Spiess and Florian 1989), and graphically represented as the shortest hyperpath on an oriented hypergraph 1 modelling the transit network (Nguyen and Pallottino 1988, Wu et al 1994.The optimal strategy is selected pre-trip and, starting from the origin, involves the iterative sequence of: walking to a public transport stop or to the destination, selecting the attractive (Nguyen and Pallottino 1988) lines to board and, for each of them, the stop where to alight. Once travelling towards the destination, if two or more attractive lines are available at a stop, the best option is to board the first approaching (Spiess 1983(Spiess , 1984.…”

mentioning

confidence: 99%

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A dynamic route choice model for public transport networks with boarding queues

Trozzi

Kaparias

Bell

et al. 2013

Transportation Planning and Technology

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link:http://openaccess.city.ac.uk/6527/ Link to published version: http://dx.doi.org/10. 1080/03081060.2012.745720 Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to. City Research Online: http://openaccess.city.ac.uk/ publications@city.ac.uk City Research OnlineA dynamic route choice model for public transport networks with boarding queuesThe concepts of optimal strategy and hyperpath were born within the framework of static frequency-based public transport assignment, where it is assumed that travel times and frequencies do not change over time and no overcrowding occurs. However, the formation of queues at public transport stops can prevent passengers from boarding the first vehicle approaching and can thus lead to additional delays in their trip. Assuming that passengers know from previous experience that for certain stops/lines they will have to wait for the arrival of the 2nd, 3rd, ..., k -th vehicle, they may alter their route choices, thus resulting in a different assignment of flows across the network. The aim of this paper is to investigate route choice behaviour changes as a result of the formation and dispersion of queues at stops within the framework of optimal travel strategies. A new model is developed, based on modifications of existing algorithms. IntroductionIt has been largely acknowledged in the last decades that urban sustainable development needs to overcome the dependence on the private car (Newman and Kenworthy 1999, European Commission 2009) and requires a modal shift towards public transport, as this leads to better performance over private transport with regards to the six sub-objectives for sustainability (May 2001 cited Black et al. 2002.In the context of promoting a transfer of passenger mobility from individual to collective means, it is important to improve the quality of service provided (i.e. total travel time reduction, increased service regularity, increased comfort on board). When this scope cannot be pursued by building more suitable infrastructure, which can be politically, financially, and environmentally constrained, Advanced Traveller Information Systems (ATIS) could play a major role if they were able to provide travellers with accurate route guidance which can consider updated and useful information about network conditions. At the present time this is not the case, as journey planners do not currently consider bus travel time which may vary with the time of the day due to recurrent road congestion. The current journey planners also do not take into account queuing time at stops and stations due to passenger congestion on the transit network. From an assignment perspective, this may lead to passenge...

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Section: Background Researchmentioning

confidence: 80%

Section: Background Researchmentioning

confidence: 99%

Section: Background Researchmentioning

confidence: 99%

Section: Attractive Setmentioning

confidence: 99%

mentioning

confidence: 99%

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A dynamic route choice model for public transport networks with boarding queues

Trozzi

Kaparias

Bell

et al. 2013

Transportation Planning and Technology

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Wardrop Equilibria

Correa

Stier-Moses

2011

Wiley Encyclopedia of Operations Research and Management Science

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Wardrop equilibria are commonly used as a solution concept of network games when modeling transportation and telecommunication networks with congestion. This concept assumes that players select a route that minimizes the time or cost incurred in its traversal. This behavioral assumption admits convenient mathematical descriptions, and efficient algorithms for the computation of equilibria are available. For this reason, planners have been making use of this concept for decades for evaluating projects, optimizing tolls, estimating demands, and a myriad applications arising from extensions of the basic model. In this article, we introduce the basic model, explain strategies for computation of equilibria, and discuss the extent of the inefficiency arising from the selfish behavior of the players. In addition, we provide some generalizations of the basic model.

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A congested and dwell time dependent transit corridor assignment model

Alonso

Muñoz

Ibeas

et al. 2016

J of Advced Transportation

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This research proposes an equilibrium assignment model for congested public transport corridors in urban areas. In this model, journey times incorporate the effect of bus queuing on travel times and boarding and alighting passengers on dwell times at stops. The model also considers limited bus capacity leading to longer waiting times and more uncomfortable journeys. The proposed model is applied to an example network, and the results are compared with those obtained in a recent study. This is followed by the analysis and discussion of a real case application in Santiago de Chile. Finally, different boarding and alighting times and different vehicle types are evaluated. In all cases, demand on express services tends to be underestimated by using constant dwell time assignment models, leading to potential planning errors for these lines. The results demonstrate the importance of considering demand dependent dwell times in the assignment process, especially at high demand levels when the capacity constraint should also be considered.

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Equilibrium traffic assignment for large scale transit networks

Cited by 324 publications

References 10 publications

A dynamic route choice model for public transport networks with boarding queues

A dynamic route choice model for public transport networks with boarding queues

Wardrop Equilibria

A congested and dwell time dependent transit corridor assignment model

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