Zero bunching solution for a local public transport system with multiple-origins bus operation

Sajikumar, S.; Bijulal, D.

doi:10.1007/s12469-021-00273-1

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…After simulating the two departure intervals against the original data, a comparative analysis revealed that the predicted interval was more in line with the road conditions and passenger flow at the time. From the prediction results, it can be seen that the recalculated departure interval algorithm is reliable and effective [10].…”

Section: Testing Of the Predicted Departure Interval Algorithmmentioning

confidence: 93%

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

Guo

Gao³

et al. 2023

E3S Web Conf.

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Urban public transportation is inseparably related to people’s travel and life. Prioritizing the development of public transportation is a major national policy proposed by the Chinese government, especially to actively promote the development of intelligent public transportation systems. Developing and building advanced intelligent public transport operation and scheduling management system, designing efficient, flexible and low-cost operation and scheduling mode, improving the management level and service quality of public transport enterprises, and thus improving the road traffic condition of the whole city, is undoubtedly the future development direction of public transport enterprises. This paper combines the problems of bus operation and scheduling in China at the present stage, and proposes the operation and scheduling model and method of the intelligent urban public transportation planning system, with a view to realizing a flexible public transportation operation model suitable for different service situations and providing systematic theoretical support for solving the imbalance between supply and demand of public transportation.

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Section: Testing Of the Predicted Departure Interval Algorithmmentioning

confidence: 93%

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

Guo

Gao³

et al. 2023

E3S Web Conf.

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show abstract

“…Most attractive for us, though for a different reason, is [38], i.e., due to the nice writing in self-coördinating as part of the title. While the original meaning of bus bunching usually refers to just one bus line, the consideration of multiple-origin bus operation can be found, e.g., in [39]. Defining bunching swings as repeating patterns of pairs of delayed and bunched vehicles can be found in [40].…”

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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“…( 2016 ) Automatic control framework using a combination of various machine learning methods Case Study for Porto (Portugal) over a period of 1 year Nguyen et al. ( 2019a ) Determine factors related to the time of initial bunching incidents for a streetcar system Case study using AVL data for Toronto (Canada) Sajikumar and Bijulal ( 2021 ) Schedule planning at certain entry points Consideration of multiple-origins bus operation Sethuraman et al. ( 2019 ) Platooning Impact analysis regarding traffic control and energy consumption Sun and Schmöcker ( 2018 ) Passenger behavior Considering overtaking being allowed (or not) Tian ( 2021 ) Use of short-turning strategy to alleviate bus bunching Case for Beijing (China) Varga et al.…”

Section: Appendixmentioning

confidence: 99%

Robustness and disturbances in public transport

Voß

Xie

2022

Public Transp

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Network-based systems are at the core of our everyday life. Whether it is electronic networking, electricity grids or transportation, users expect the networks to function properly and provide a feeling of safety and security. However, there may be disturbances. In this paper, we consider disturbances in the context of public transportation. The focus in this respect is on public transport planning and operations. To classify and cope with disturbances, one can find many ideas, including robustness, resilience, vulnerability, disruption mitigation or delay management. We survey related streams of literature and put them into perspective. As a major insight we show that different strands of literature exist that may benefit from becoming better connected and intertwined. Together with recent advances in information technology and solution methods, more integrated problem settings incorporating robustness and disturbances can play a major role in future planning and operations.

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Zero bunching solution for a local public transport system with multiple-origins bus operation

Cited by 6 publications

References 23 publications

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

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

Robustness and disturbances in public transport

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