A Dynamic Multiagent Simulation of Mobility in a Train Station

Boulet, Xavier; Zargayouna, Mahdi; Leurent, Fabien; Kabalan, Bachar; Ksontini, Feirouz

doi:10.1016/j.trpro.2017.12.100

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…3) Coordination of the demand for commuter passenger transportation with the schedule of electric trains (marketing complex of tasks) is a set of tasks for studying the demand for transportation formed by the structure of passenger flows [8,16 ] based on targeted travel of passengers, and the development of schedules of electric trains that are adequate (fully satisfying) the needs of the main categories of passengers on trips. 4) Passenger transportation fare collection (operational unit of ensuring the collection passengers' fares) is the largest unit in terms of the number of employees.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the planning of staffing the electric trains with teams of inspectors-cashiers should take into account these two factors: the rate of the served passenger flow and the state of its structure in terms of how many passengers boarding the electric train have tickets. The iterative nature of the daily logistics cycle of management's operational activities in the organization and implementation of inspecting and cashing services for trains on commuter routes determines adequate continuous information maintenance, supported by a self-improving information platform and information technologies of iterative nature as well [8,9]. In previous studies of the problem of collecting the fare for commuter transportation, the need was shown for building and using a set of models that bring operational inspecting and cashing activities in commuter transportation to an effective level of organization [6].…”

Section: Publications Review and Research Methodsmentioning

confidence: 99%

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Digital Technologies Preconditions in Traffic Management and Passengers’ Fare Collection on Commuter Trains

Sankov¹,

Morozov²,

Bochkarev³

2020

Proceedings of the “New Silk Road: Business Cooperation and Prospective of Economic Development” (NSRBCPED 2019)

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The relevance ensuring collection of transportation fares from passengers in full by Suburban Passenger Companies (SPC) is an especially important aspect of their operation as it forms the companies' income in its fullness from their main activity. It can be effective only on the assumption of comprehensive integrated interaction of passenger flows and inspecting-cashing apparatus. As the functioning of passenger flows has a random, uncontrollable nature, it is the primary, determining element in the system to be served and submits a dynamic demand for generating an adequate reactive inspectingcashing impact on the flow, by the criterion of completeness of revenue collectionthat is, ensuring the required quality of their service in aspect of the revenue collection. The research methods were the author's developed complex of models for constructing particular models necessary for the effective solution of individual tasks of managing the process of inspecting-cashing management on suburban passenger transportation. The result of the study is the theoretical justification for solving the company's internal target -to achieve the most complete collection of passenger transportation fees according to the criterion for the company in the form of a minimum amount of costs for the maintenance and operation of the service and losses on unearned income for the transportation of passengers.

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Section: Resultsmentioning

confidence: 99%

Section: Publications Review and Research Methodsmentioning

confidence: 99%

Digital Technologies Preconditions in Traffic Management and Passengers’ Fare Collection on Commuter Trains

Sankov¹,

Morozov²,

Bochkarev³

2020

Proceedings of the “New Silk Road: Business Cooperation and Prospective of Economic Development” (NSRBCPED 2019)

View full text Add to dashboard Cite

show abstract

“…Takagi-Sugeno-type fuzzy rules have been used in the suggested fuzzy model to convert physical laws of pedestrian motion into a fuzzy inference system FIS [73]. Moreover, high congestion in railway stations affects the quality of service for travellers and damage might extend to the business images, or worse, it may subsequently interrupt operational continuity because of the growth of the dwell time [20,74,75]. Furthermore, high levels of passenger density have been connected to many psychological and social issues, including perceptions of risk to personal safety and security [12,76], increased anxiety [24], stress, feeling of exhaustion, and the possibility of ill health [22,25,77].…”

Section: Overcrowding At Railway Stationsmentioning

confidence: 99%

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

Alawad

Kaewunruen

2020

Applied Sciences

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The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

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Incomplete Passenger Fare at Commuter Trains

Sankov

Morozov

2019

Smart Technologies and Innovations in Design for Control of Technological Processes and Objects: Economy and Production

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A Dynamic Multiagent Simulation of Mobility in a Train Station

Cited by 4 publications

References 18 publications

Digital Technologies Preconditions in Traffic Management and Passengers’ Fare Collection on Commuter Trains

Digital Technologies Preconditions in Traffic Management and Passengers’ Fare Collection on Commuter Trains

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

Incomplete Passenger Fare at Commuter Trains

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