Skip to Main content Skip to Navigation
Conference papers

How to collect data to simulate the dynamic of trains-passengers' interaction

Abstract : This paper presents a motivation-based model in order to explore crowd behavior. The case study is about what motivates the decision processes of passengers about choice of location on the station platform for ingressing and egressing trains.The goal of theresearch is twofold: to establisha cognitive generic crowd behavior modeling method and torespond to a major challenge of public transportation: to reduce dwell time to ensure a high level of service.We first introduce motivation-based modeling for the simulation of the dynamics of numerous cognitive agents and report the collection ofpassengers’ dynamics that was done through an extensive survey observation.Most significant variableswere then extracted fromfactoranalysis to compose and distinguish six main motivation based strategiesthat are to be used for the simulation of crowd behavior in the train station.Discussion is about the advantages of motivation-based simulation in terms of robustness and adaptability and conclusion abouthow Artificial Intelligence, Cognitive Psychology and Data Science operate together to model such complex systems.
Complete list of metadata
Contributor : Axel Buendia Connect in order to contact the contributor
Submitted on : Monday, August 23, 2021 - 11:59:42 AM
Last modification on : Wednesday, September 28, 2022 - 5:58:27 AM


  • HAL Id : hal-03324117, version 1


Axel Buendia, Fatma Elleuch, Stéphanie Donnet, Charles Tijus, Stéphane Natkin. How to collect data to simulate the dynamic of trains-passengers' interaction. COGSCI2018 40th annual cognitive science society meeting, Cognitive Science Society, Jul 2018, Madison, United States. pp.1645-1650. ⟨hal-03324117⟩



Record views