A novel control strategy in mitigating bus bunching: Utilizing real-time information

Zhou, Chen; Qiu, Tian; Wang, David Z.W.

doi:10.1016/j.tranpol.2022.04.022

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…In addition to vehicle holding, other methods have been developed to manage headway variability in real time, whereby the most commonly studied methods are based on allowing buses to overtake each other (Wu et In one of the recent studies dealing with the control strategy based on real-time information, Zhou et al (2022) identi ed a signi cant gap between the recommendations based on recent theories and modern practice. According to these authors, closing this gap requires the adoption of appropriate technology and software for continuous communication between the control centre and the driver.…”

Section: Literature Reviewmentioning

confidence: 99%

Planning-level optimisation of headway regularity

Pitka,

Simeunović,

Miličić

et al. 2024

Preprint

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Headway variability has a negative impact on the public transport passengers' perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them in practice. On the other hand, planning-level methods can be used to increase the resilience of public passenger transport (PPT) to the accumulation of headway disturbances. As this is typically done from the operator's perspective, the passengers' perspective tends to be overlooked, motivating the current work. In this article, an optimisation procedure for evaluating the viability of diametrical line splitting in terms of passenger travel time and headway regularity is proposed. The aim is to increase the robustness/resistance of the PPT system to the propagation of headway disturbances without reducing the service quality. The developed optimisation procedure was validated by applying it to real data pertaining to an urban PPT line. The results show that there is a positive correlation between the transport demand and the effects of the optimisation procedure, whereby an increase in the primary headway disturbance increases the sensitivity of the optimisation procedure to the transport demand.

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Section: Literature Reviewmentioning

confidence: 99%

Planning-level optimisation of headway regularity

Pitka,

Simeunović,

Miličić

et al. 2024

Preprint

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“…Very recent papers beyond [5] include the following. In [44], the idea is that passengers are provided with real-time waiting time information and degrees of in-vehicle congestion. If properly applied, the passengers' boarding choice behavior is adjusted rather than controlling the buses themselves.…”

Section: Bus Bunchingmentioning

confidence: 99%

Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?

Voß

2023

Sustainability

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Regarding tools and systems from artificial intelligence (AI), chat-based ones from the area of generative AI have become a major focus regarding media coverage. ChatGPT and occasionally other systems (such as those from Microsoft and Google) are discussed with hundreds if not thousands of academic papers as well as newspaper articles. While various areas have considerably gone into this discussion, transportation and logistics has not yet come that far. In this paper, we explore the use of generative AI tools within this domain. More specifically, we focus on a topic related to sustainable passenger transportation, that is, the handling of disturbances in public transport when it comes to bus bunching and bus bridging. The first of these concepts is related to analyzing situations where we observe two or more buses of the same line following close to each other without being planned deliberately and the second is related to the case where buses are used to replace broken connections in other systems, such as subways. Generative AI tools seem to be able to provide meaningful entries and a lot of food for thought while the academic use may still be classified as limited.

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“…Bus realtime information, such as in-vehicle congestion and waiting time information, helps to mitigate bus bunching with passengers guided to choose the most appropriate bus, instead of the earliest one. Building a simulation model to depict bus operation and passenger service choices, Zhou et al [15] showed that providing bus real-time information is as effective as the schedule-based and headway-based control methods in reducing passenger waiting time. Zhang et al [16] studied an automatic bus schedule redesign method based on bus arrival time prediction for real-time bus dispatching to minimize the impact on the initial schedule by finding the minimum adjustment range, which reduces passengers' average wait time at stops and allows bus operations to be adjusted in time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Route Assignment at the Bus Hub with Multiple Berths for Minimal Passenger Transfer Distance

Zhang

Jin

Hao

et al. 2022

Journal of Mathematics

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Bus hub is characterized with multiple berths, each serving specific bus routes. When bus routes at different berths share the same stops en route, passengers to the shared stops may rush among these berths to catch the newly arriving bus, causing inconvenience and deteriorating bus service level. Thus, this research attempts to optimize bus route assignment to the berths at a bus hub to minimize passenger transfer distance among the optional berths. The problem is modeled with integer linear programming, with the constraints of bus service capacity. To enhance computation efficiency, exclusive and inclusive constraints based on the count of shared stops among the bus routes are additionally added to the original model, though they may bring gap from the optimal solution. Case study follows to apply the proposed set of models to a bus terminal, where a total of 26 routes are assigned to 11 berths. It is found that the models with additional constraints can greatly reduce calculation time at the cost of small or no gap from the optimal solution. Thus, the models with additional constraints are recommended to obtain ideal solution quickly. This research provides insights into bus route management at the bus terminal with multiple berths, contributing to enhanced passenger waiting experience.

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A novel control strategy in mitigating bus bunching: Utilizing real-time information

Cited by 9 publications

References 47 publications

Planning-level optimisation of headway regularity

Planning-level optimisation of headway regularity

Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?

Optimal Route Assignment at the Bus Hub with Multiple Berths for Minimal Passenger Transfer Distance

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