A game theoretic model for re-optimizing a railway timetable

Fragnelli, Vito; Sanguineti, Simona

doi:10.1007/s12544-013-0116-y

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…Also, some principles of natural swarm intelligence in the development of artifcial systems can be used to solve the complex multiagent system in transportation planning and management [27], including but not limited to the railway industry. In the background of competitive railway market in 1990s, the authors in [28] proposed a game theoretical model for a coalition formation problem. Also, the agents (i.e., transport operators) in the model could exchange information on their needs and be rewarded by a possible increasing of their utility.…”

Section: Paper Reviews By Adversarialmentioning

confidence: 99%

Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective

Zhang

2023

Discrete Dynamics in Nature and Society

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Artificial intelligence (AI) has received much attention in the domain of railway traffic planning and management (TPM) from academia and industries. While many promising applications have been reported, there remains a lack of detailed review of the many AI models/algorithms and their uses and adaptations in rail TPM. To fill this gap, this systematic literature review conducts, reports, and synthesizes the state-of-the-art of AI applied in railway TPM from four perspectives, i.e., the intersection between AI research fields (e.g., expert systems, data mining, and adversarial search) and rail TPM, the intersection between AI techniques (e.g., evolutionary computing and machine learning) and rail TPM, the intersection between AI applications (e.g., operations research, scheduling, and planning) and rail TPM, and the intersection between AI related disciplines (e.g., big data analytics and digital twins) and rail TPM. The study evaluates 95 research papers published during 1970–2022. Accordingly, a comprehensive synthesis of each intersection between AI and rail TPM is presented, and the practical roadmap for application of AI in rail TPM is proposed. Furthermore, the study identifies the research gaps and areas that need more investigation. The contribution helps researchers and practitioners to get a better understanding of the status quo of research stream, research development trends, and challenges for further related study.

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Section: Paper Reviews By Adversarialmentioning

confidence: 99%

Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective

Zhang

2023

Discrete Dynamics in Nature and Society

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“…performance assessment [60] delay pattern recognition [61] train dispatching [62] Y: train rescheduling [63] Y train timetabling [64] Revenue management U P: revenue forecasting [65] RM system design [66] U P: RM simulation [67] pricing [68] Transport policy P:…”

Section: A Existing Applicationsmentioning

confidence: 99%

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Bešinović

Donato

Flammini

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

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“…A nontransferable utility game theoretical study is proposed by Fragnelli and Sanguineti (2014), who examine the need for information exchange for cooperative timetabling among multiple train operators. Companies exchange preferences about their needs and get compensation under limited information disclosure.…”

Section: Game Theoretical Studies In Transit Ormentioning

confidence: 99%

Interactions and Equilibrium Between Rescheduling Train Traffic and Routing Passengers in Microscopic Delay Management: A Game Theoretical Study

Corman

2020

Transportation Science

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In the last decade, optimization models for railway traffic rescheduling mostly focused on incorporating an increasing detail of the infrastructure, with the goal of proving feasibility and quality from the point of view of the managers of the infrastructure (tracks and stations). Different approaches that manage only the passenger flows instead focus more explicitly on the quality of service perceived by the passengers. This paper investigates microscopic railway traffic optimization models and algorithms, merging these two streams of research. In particular, we analyze the characterization of an equilibrium point between the reordering choices of train dispatchers in railway traffic optimization and the route choice of passengers in the available services of the railway transport network. We describe how passenger choice at stations along the route intertwines deeply with the problem of rescheduling trains over tracks and station resources in a very complicated setting that might not exhibit equilibrium points in general. Delaying trains and/or dropping passenger connections and/or giving particular route advice to passengers might influence the behavior of traffic controllers and passengers, determining a trade-off between the delays of trains, weighted by the passenger load, and the travel time of passengers. We study this problem with a game theoretical approach, focusing on the solutions corresponding to Nash equilibria of a game involving passengers and infrastructure managers. The proposed game theoretical approach is able to easily consider information and interdependence of the actions of multiple stakeholders. Computational results based on a real-world Dutch railway network quantify the trade-off between the minimization of train delays and passenger travel times and the performance, stability, and convergence of the equilibrium point given different algorithms and information available. The final aim of this work is to study the impact of effective implementations of railway traffic management and dissemination of information to passengers and operators.

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A game theoretic model for re-optimizing a railway timetable

Cited by 10 publications

References 26 publications

Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective

Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Interactions and Equilibrium Between Rescheduling Train Traffic and Routing Passengers in Microscopic Delay Management: A Game Theoretical Study

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