Analysis and simulation of an urban bus route

Andersson, Per-Åke; Hermansson, Åke; Tengvald, Erik; Scalia‐Tomba, Gianpaolo

doi:10.1016/0191-2607(79)90007-4

Cited by 36 publications

(12 citation statements)

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“…The second is that the behavior of a global system consists of phenomena derived from the behavior of separate entities, as well as from the interactions among the entities [67][68][69]. Andersson et al (1979) [70] proposed an interactive simulation model to assess a city bus route during a peak traffic jam. This model can serve as a tool and guide for route control operators by helping them to understand how each individual act impacts the service.…”

Section: Simulation Model As a Tool To Analyzing Bus Service Reliabilitymentioning

confidence: 99%

Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies

2020

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Bus services naturally tend to be unstable and are not always capable of adhering to schedules without control strategies. Therefore, bus users and bus service providers face travel time variation and irregularity. After a comprehensive review of the literature, a significant gap was recognized in the field of public transportation reliability. According to literature, there is no consistency in reliability definition and indicators. Companies have their own definition of bus service reliability, and they mostly neglect the passengers' perspective of reliability. Therefore, four reliability indicators were selected in this study to fill the gap in the literature and cover both passengers' and operators' perceptions of reliability: waiting time and on-board crowding level from passengers' perspective, and headway regularity index at stops (HRIS) and bus bunching/big gap percentage from operators' perspective. The primary objective of this research is to improve the reliability of high frequency of bus service and simulation tools currently being used by the public transportation companies. Therefore, a simulation model of bus service was developed to study the strategies to alleviate it. Four different types of strategies were selected and implemented according to Route U32 (Kuala Lumpur) specifications. Model output showed that control strategies such as headwaybased dispatching could significantly improve headway regularity by almost 62% and the waiting time by 51% on average. Both holding strategies at key stops (previous and Prefol holding) have shown an almost similar impact on reliability indicators. Waiting time was reduced by 44% and 43% after the previous and Prefol Headway strategies were adopted, respectively. However, the implementation of the component of headway-based strategies at the terminal and key stops showed the best impact on reliability, in terms of passenger waiting time. Waiting time and excess waiting time were both significantly reduced by 52.86% and 81.44%, respectively. Nevertheless, the strategies did not show any significant positive effect on the level of crowding during morning peak hours.

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Section: Simulation Model As a Tool To Analyzing Bus Service Reliabilitymentioning

confidence: 99%

Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies

2020

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“…The output of the simulation model is used as an input to a decision model which selects the optimum redesign and/or ramp control strategies. An interactive simulation model designed to route buses in an urban environment during rush hour traffic was reported by Andersson et al [3]. An on-line system is used to collect data from the actual bus traffic.…”

Section: Evaluation Of Design Alternatives For a New System And Of Almentioning

confidence: 99%

Simulation as a Tool for the Analysis and Design of Systems

Levary¹

1986

European Journal of Engineering Education

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“…They can be categorized into three families of methods: (i) stochastic; (ii) static; and (iii) simplified dynamic models. (i) Bus‐focused stochastic models resort to probabilistic distributions to generate travel times of buses . They allow reproducing the variability of traffic conditions, passenger demand, and transit operations.…”

Section: Introductionmentioning

confidence: 99%

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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Analysis and simulation of an urban bus route

Cited by 36 publications

References 1 publication

Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies

Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies

Simulation as a Tool for the Analysis and Design of Systems

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

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