Impacts of Holding Control Strategies on Transit Performance

Cats, Oded; Larijani, Anahid Nabavi; Koutsopoulos, Haris N.; Burghout, Wilco

doi:10.3141/2216-06

Cited by 104 publications

(93 citation statements)

References 18 publications

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“…The commonly applied delay preservation prediction scheme assumes AT j,k+1 = AT m,k+1 + SRT mj , where AT m,k+1 denotes the arrival time at the last visited stop m and SRT mj is the scheduled riding time between m and j. Parameter α is a threshold ratio that determines the minimum allowed headway with values varying from 0.6 to 0.8 as found by previous studies [3,7]. In previous simulation [7,14] and field experiment [9] studies, the even headway strategy outperformed schedule-based strategies and demonstrated robust results.…”

Section: B Even Headway Holding Strategymentioning

confidence: 99%

“…The even headway strategy is currently used for the trunk lines in Stockholm. This strategy was implemented following a series of simulation and field experiment studies [7]. An empirical analysis of the performance of this strategy demonstrated that it resulted in passenger travel time savings when compared with the previous schedule-based holding control.…”

Section: B Scenario Designmentioning

confidence: 99%

“…The even headway strategy regulates departure times considering the proceeding and the succeeding vehicle on the same line and also limits the maximum holding time [7]. In order to be implemented, real time information on vehicle locations is needed.…”

Section: B Even Headway Holding Strategymentioning

confidence: 99%

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A real-time holding decision rule accounting for passenger travel cost

Laskaris

Cats

Jenelius

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Holding has been extensively investigated as a strategy to mitigate the inherently stochastic nature of public transport operations. Holding focuses on either regulating vehicle headways using a rule-based approach or minimizing passenger travel cost by employing optimization models. This paper introduces a holding decision rule that explicitly addresses passenger travel cost. The decision to hold relies on the passenger demand distribution along the line. The passenger cost holding rule is tested using simulation for a high frequency bus line in Stockholm, Sweden and is compared with a nocontrol scheme and the currently used headway-based strategy. The results indicate that the new decision rule results in relatively minor reductions of passenger cost compared to the currently adopted strategy, and that it allocates the greatest share of holding time at the beginning of the route.

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Section: B Even Headway Holding Strategymentioning

confidence: 99%

Section: B Scenario Designmentioning

confidence: 99%

See 1 more Smart Citation

A real-time holding decision rule accounting for passenger travel cost

Laskaris

Cats

Jenelius

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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show abstract

“…Finding the optimal holding strategy has been the focus of many studies, see a review of strategies in Strathman et al (2001). Cats et al (2012) test different holding strategies in terms of holding criteria and time point and find that headway-based strategies are superior to schedule-based strategies. Reliability is further improved by adapting holding to both the preceding and following bus.…”

Section: Planning and Technology Scenariosmentioning

confidence: 99%

“…Focusing on transit operations, Toledo et al (2010) evaluate the effects of varying passenger demand and travel time uncertainty on on-time performance and headway reliability of transit vehicles. Cats et al (2012) and Fernandez et al (2010) investigates the effects of various holding strategies on passengers in terms of headway variability, travel time and waiting times. Cats (2016) evaluate the effects of a network extension on crowding in transit vehicles.…”

Section: The Proposed Modelmentioning

confidence: 99%

Analysing improvements to on-street public transport systems: a mesoscopic model approach

Ingvardson

Jensen

2017

Public Transp

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Light rail transit and bus rapid transit have shown to be efficient and costeffective in improving public transport systems of cities around the world. As these systems comprise various elements, which can be tailored to any given setting, e.g. pre-board fare-collection, holding strategies and other Advanced Public Transport Systems (APTS), the attractiveness of such systems depend heavily on their implementation. In the early planning stage it is advantageous to deploy simple and transparent models to evaluate possible ways of implementation. For this purpose, the present study develops a mesoscopic model which makes it possible to evaluate public transport operations in details, including dwell times, intelligent traffic signal timings and holding strategies while modelling impacts from other traffic using statistical distributional data thereby ensuring simplicity in use and fast computational times. This makes it appropriate for analysing the impacts of improvements to public transport operations, individually or in combination, in early planning stages. The paper presents a joint measure of reliability for such evaluations based on passengers' perceived travel time by considering headway time regularity and running time variability, i.e. taking into account waiting time and in-vehicle time. The approach was applied on a case study by assessing the effects of implementing segregated infrastructure and APTS-elements, individually and in combination. The results showed that the reliability of on-street public transport operations mainly depends on APTS-elements, and especially holding strategies, whereas pure infrastructure improvements induced travel time reductions. The results further suggested that synergy effects can be obtained by planning on-street public transport coherently in terms of reduced travel times and increased reliability.

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Investigating the irregularity of bus routes: highlighting how underlying assumptions of bus models impact the regularity results

Hans

Chiabaut

Leclercq

2014

J of Advced Transportation

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Summary A bus route is inherently unstable: when the system is uncontrolled, buses fail to maintain their time‐headways and tend to bunch. Several mathematical bus motion models were proposed to reproduce the bus behavior and assess management strategies. However, no work has established how the choice of a model impacts the irregularity of modeled bus systems, that is, the non‐respect of scheduled headways. Because of this gap, a large body of existing works assumes that the ability of these models to reproduce instability comes only from stochasticity, although the link between stochastic inputs and the level of irregularity remains unknown. Moreover, some recognized phenomena such as a change of travel conditions during a day or delays at signalized intersections are ignored. To address these shortcomings, this paper provides an overview of existing dynamic bus‐focused models and proposes a simple way to classify them. Commonly used deterministic and stochastic models are compared, which allows quantifying the relative influence of stochasticity of each model component on outputs. Moreover, we show that a change in the system equilibrium in a full deterministic system can lead to irregularity. Finally, this paper proposes a refinement of travel time models to account for non‐dynamic signals. In presence of traffic signals, we show that a bus system can be self‐regulated. Especially, these insights could help to calibrate bus model inputs to better reproduce real data. Copyright © 2014 John Wiley & Sons, Ltd.

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Impacts of Holding Control Strategies on Transit Performance

Cited by 104 publications

References 18 publications

A real-time holding decision rule accounting for passenger travel cost

A real-time holding decision rule accounting for passenger travel cost

Analysing improvements to on-street public transport systems: a mesoscopic model approach

Investigating the irregularity of bus routes: highlighting how underlying assumptions of bus models impact the regularity results

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