Modeling of passengers’ choice using intelligent agents with reinforcement learning in shared interests systems; A basic approach

The purpose of this paper is to build a model for assessing the satisfaction of passenger service by the public transport system. The system is constructed using intelligent agents, whose action is based on self-learning principles. The agents are passengers who depend on transport and can choose between two modes: a car or a bus wherein their choice of transport mode for the next day is based on their level of satisfaction and their neighbors’ satisfaction with the mode they used the day before. The paper considers several algorithms of agent behavior, one of which is based on reinforcement learning. Overall, the algorithms take into account the history of the agents’ previous trips and the quality of transport services. The outcomes could be applied in assessing the quality of the transport system from the point of view of passengers.

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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).