Capacity Constrained Transit Assignment with Common Lines

Kurauchi, Fumitaka; Bell, M.G.; Schmöcker, Jan-Dirk

doi:10.1023/b:jmma.0000020426.22501.c1

Cited by 111 publications

(61 citation statements)

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“…On the other hand, Cominetti and Correa (2001) and Cepeda et al (2006) model waiting time as inversely proportional to the effective frequency, which is a function of the actual frequency that decreases with the occupancy rate of buses upstream of a bus stop. The assignment model of Kurauchi et al (2003) introduces that passengers may be risk-averse in their behaviour regarding what line or service to use, and therefore, be more prone to choose routes in which occupancy levels are lower, as a way to reduce the chance of failing to board a bus (for the effect of sitting and standing probabilities on route choice, see Section 2.6). In real-world applications, the increase in waiting time due to capacity constraints has been considered in the estimation of public transport load and demand in large scale scenarios including London (Department of Transport, 1989;Maier, 2011), Winnipeg, Stockholm and Santiago de Chile (Florian et al, 2005), Los Angeles and Sydney (Davidson et al, 2011) and San Francisco (Zorn et al, 2012).…”

Section: Effect On Waiting Timementioning

confidence: 99%

Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

Tirachini

Hensher

Rose

2013

Transportation Research Part A: Policy and Practice

217

153

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The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. . This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers' wellbeing, valuation of waiting and invehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. 2

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Section: Effect On Waiting Timementioning

confidence: 99%

Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

Tirachini

Hensher

Rose

2013

Transportation Research Part A: Policy and Practice

217

153

View full text Add to dashboard Cite

show abstract

“…and their values are shown in Marcotte and Nguyen 1998, Cominetti and Correa 2001, Kurauchi et al 2003, Cepeda et al 2006, Schmöcker et al 2008, Leurent and Benezech 2011. This behavioural assumption implies that no waiting priority is respected and, in case of oversaturation, all passengers waiting at a stop have the same probability to board the next carrier to approach (provided the carrier is attractive).…”

Section: Background Researchmentioning

confidence: 99%

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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“…For example, during peak-hours passengers often experience an over-saturation waiting time at stops, because they are not able to board the first vehicle of their choice set that arrives. The queue of those who remain at the stop may also increase passenger congestion for subsequent vehicle arrivals, and thus lead to great Level Of Service (LOS) variations that cannot be properly captured by static models, even when capacity constraints are considered [7][8][9]11].…”

Section: Introductionmentioning

confidence: 99%

“…While strategy-based transit assignment with capacity constraints has been treated in a static framework [7][8][9]11], only few dynamic models exist [10]. Moreover, previous work has dealt with capacity constraints in FB transit assignment with hyperpaths, either assuming that the extra waiting may affect the users' perception of service frequency, or that crowding may increase the perceived cost of travelling through the fail-to-board probability.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, previous work has dealt with capacity constraints in FB transit assignment with hyperpaths, either assuming that the extra waiting may affect the users' perception of service frequency, or that crowding may increase the perceived cost of travelling through the fail-to-board probability. The first method, formally known as effective frequency, has been developed only in a static framework, either considering the "practical capacity" of the transit lines [7] or strict capacity constrains [8,9]; on the other hand the method of fail-to-board probability [10,11] has been developed also in a dynamic framework. All the afore-mentioned methods assume that queuing takes the form of passengers mingling at the stop, without respecting any boarding priority.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic user equilibrium in public transport networks with passenger congestion and hyperpaths

Trozzi

Gentile

Bell

et al. 2013

Transportation Research Part B: Methodological

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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 AbstractThis paper presents a Dynamic User Equilibrium for bus networks where recurrent overcrowding results in queues at stops. The route choice model embedded in the dynamic assignment explicitly considers common lines and strategies with alternative routes. As such, the shortest hyperpath problem is extended to a dynamic scenario with capacity constraints where the diversion probabilities depend on the time the stop is reached and on the expected congestion level at that time. In order to reproduce congestion for all the lines sharing a stop, the Bottleneck Queue model with time-varying exit capacity, introduced in Meschini et al. (2007), is extended. The above is applied to separate queues for each line in order to satisfy the First-In-First-Out principle within every attractive set, while allowing overtaking among passengers having different attractive sets but queuing single file.

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Capacity Constrained Transit Assignment with Common Lines

Cited by 111 publications

References 10 publications

Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

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

Dynamic user equilibrium in public transport networks with passenger congestion and hyperpaths

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