Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem

Leprêtre, Florian; Fonlupt, Cyril; Vérel, Sebástien; Marion, Virginie

doi:10.1007/978-3-030-45715-0_4

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…To assure convergence for larger networks, it would require improved efficiency of the optimization technique. Promising results in the fields of combinatorial optimization point out potential research directions (Lepretre et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Simulation-Based Design of Urban Bi-modal Transport Systems

Tilg¹,

Abedin²,

Amini³

et al. 2020

Front. Future Transp.

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The three-dimensional passenger macroscopic fundamental diagram (pMFD) describes the relation of the network accumulation of public transport and private vehicles, and the passenger production. It allows for modeling the multi-modal traffic dynamics in urban networks and deriving innovative performance indicators. This paper integrates this concept into a multi-modal transport system design framework formulated as a simulation-based optimization problem. In doing so, we consider the competition for limited road space and the operational characteristics, such as congestion occurrences, at the strategic design level. We evaluate the proposed framework in a case study for the Sioux Falls network. Thereby, we deliver a proof of concept, and show that the proposed methodology indeed designs a transport system which benefits the overall system's performance. This paper further advances the integration of sequential model-based optimization techniques, macroscopic traffic flow concepts, and traffic simulation to design multi-modal transport systems. This supports transport planners and local authorities in composing efficient and robust transport networks.

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Section: Resultsmentioning

confidence: 99%

Simulation-Based Design of Urban Bi-modal Transport Systems

Tilg¹,

Abedin²,

Amini³

et al. 2020

Front. Future Transp.

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“…The bus stops spacing problem is a challenge in the field of transportation modeling [23,43,44,31,32,22]. It involves determining the optimal placement of bus stops in a public transportation system within a certain area.…”

Section: Bus Stops Spacing Problemmentioning

confidence: 99%

“…It involves determining the optimal placement of bus stops in a public transportation system within a certain area. Many approaches to this problem exist in order to optimize different criteria of public transportation such as passengers travel time [23,43,44], economic costs [31,22], or environmental impact [32].…”

Section: Bus Stops Spacing Problemmentioning

confidence: 99%

Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem

Vendi,

Verel,

Fonlupt

2024

Lecture Notes in Computer Science

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Combinatorial optimization problems can involve computationaly expensive fitness function, making their resolution challenging. Surrogate models are one of the effective techniques used to solve such black-box problems by guiding the search towards potentially good solutions. In this paper, we focus on the use of surrogate based on multinomial approaches, particularly based on Walsh functions, to tackle pseudo-Boolean problems. Although this approach can be effective, a potential drawback is the growth of the polynomial expansion with problem dimension. We introduce a method for analyzing real-world combinatorial black-box problems defined through numerical simulation. This method combines Walsh spectral analysis and polynomial regression. Consequently, we propose a sparse surrogate model that incorporates selected, relevant terms and is simpler to optimize. To demonstrate our approach, we apply it to the bus stop spacing problem, an exemplary combinatorial pseudo-Boolean challenge.

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“…• Supporting PT system planning (Degeler et al 2020;Leprêtre et al 2019;Mackett 1994Mackett , 1996Roulland et al 2014;Shatnawi et al 2020;Ullón et al 2020;Van Egmond et al 2003). • Timetable scheduling support (Bembalkar and Game 2019;Othman and Tan 2018;Tan et al 2011;Tekin et al 2018;Xie et al 2004).…”

Section: Operationsmentioning

confidence: 99%

Artificial intelligence for improving public transport: a mapping study

Jevinger,

Zhao,

Persson

et al. 2023

Public Transp

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The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

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Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem

Cited by 7 publications

References 19 publications

Simulation-Based Design of Urban Bi-modal Transport Systems

Simulation-Based Design of Urban Bi-modal Transport Systems

Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem

Artificial intelligence for improving public transport: a mapping study

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