Highlights This paper investigates adaptive forecasting for parking space availability. It models time-varying arrival/departure behavior by a regime switching process. The adaptive forecasting is based on the currently recognized traffic regime. The developed adaptive forecasting method is tested using out-of-sample data.

    Abstract Parking space availability is valuable information to travelers. This paper aims at modeling drivers’ behavioral changes in arrivals/departures over time of day and developing an adaptive forecasting approach for parking space availability. We propose a stochastic model that consists of two inter-connected Markov processes. First, the lower level of the model focuses on the parking behavior within a short time period, based on conventional M/M/C/C queueing theory with the assumption of fixed arrival and parking rates. Next, to account for the behavioral changes in drivers’ arrivals/departures over a longer time period (e.g. time of day), we incorporate a Markov regime switching process to describe the regime switching mechanism of the arrival/departure behavior. The integrated model leads to an adaptive forecasting formula with time-varying forecasting coefficients adaptively adjusted based on the arrival/departure regimes. We investigate two real traffic applications to illustrate the developed stochastic model and to test the performance of the adaptive forecasting method using out-of-sample data.


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    Titel :

    Stochastic modeling and adaptive forecasting for parking space availability with drivers’ time-varying arrival/departure behavior


    Beteiligte:
    Li, Baibing (Autor:in)


    Erscheinungsdatum :

    2022-10-31


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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