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

Baibing Li

Transportation Research Part B: Methodological, 2022, vol. 166, issue C, 313-332

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.

Keywords: Adaptive forecasting; Markov switching process; Parking space availability; Timing-varying arrival/departure behavior (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.trb.2022.10.014

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