The number of vehicles in urban cities has increased and raised attention towards the need for effective parking lot management in public areas such as hospital, shopping mall and office building. In this study, dynamic pricing is deployed with real time parking information to maximize the parking usage rate and alleviate traffic congestion. Dynamic pricing is a practice of varying the price of product of service reflected by the market conditions. This technique can be used to deal with vehicle flow around the parking area including peak and non-peak hour. During peak hours, the dynamic pricing mechanism will regulate the price of parking fee to a relatively high rate, and vice versa for non-peak hours. Reinforcement Learning (RL) is used in this paper to develop a dynamic pricing model for parking management. Dynamic pricing over time is divided into episodes and shuffled back and forth through an hourly increment. The parking usage rate and traffic congestion rate are regarded as the rewards for price regulation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Dynamic Pricing for Parking System Using Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Kim, Hyuncheol (Herausgeber:in) / Kim, Kuinam J. (Herausgeber:in) / Park, Suhyun (Herausgeber:in) / Poh, Li Zhe (Autor:in) / Tee, Connie (Autor:in) / Ong, Thian Song (Autor:in) / Goh, Michael (Autor:in)


    Erscheinungsdatum :

    2021-04-03


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Dynamic Parking Pricing

    Willson, Richard / Irish, Aiden | Transportation Research Record | 2016



    Autonomous Car Parking System using Deep Reinforcement Learning

    Takehara, Rikuya / Gonsalves, Tad | IEEE | 2021


    Optimal dynamic pricing for morning commute parking

    Qian, Zhen (Sean) / Rajagopal, Ram | Taylor & Francis Verlag | 2015