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

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  • Li, Baibing

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.

Suggested Citation

  • Li, Baibing, 2022. "Stochastic modeling and adaptive forecasting for parking space availability with drivers’ time-varying arrival/departure behavior," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 313-332.
    • Handle: RePEc:eee:transb:v:166:y:2022:i:c:p:313-332
    DOI: 10.1016/j.trb.2022.10.014
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    References listed on IDEAS

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    1. Shoup, Donald C., 2006. "Cruising for parking," Transport Policy, Elsevier, vol. 13(6), pages 479-486, November.
    2. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    3. Shoup, Donald C., 2006. "Cruising for Parking," University of California Transportation Center, Working Papers qt55s7079f, University of California Transportation Center.
    4. Bebu, Ionut & Mathew, Thomas, 2009. "Confidence intervals for limited moments and truncated moments in normal and lognormal models," Statistics & Probability Letters, Elsevier, vol. 79(3), pages 375-380, February.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Tayo Fabusuyi & Robert C. Hampshire & Victoria A. Hill & Katsunobu Sasanuma, 2014. "Decision Analytics for Parking Availability in Downtown Pittsburgh," Interfaces, INFORMS, vol. 44(3), pages 286-299, June.
    7. Xiao, Jun & Lou, Yingyan & Frisby, Joshua, 2018. "How likely am I to find parking? – A practical model-based framework for predicting parking availability," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 19-39.
    8. Vito Albino & Umberto Berardi & Rosa Maria Dangelico, 2015. "Smart Cities: Definitions, Dimensions, Performance, and Initiatives," Journal of Urban Technology, Taylor & Francis Journals, vol. 22(1), pages 3-21, January.
    9. Burns, Malcolm R. & Faurot, David J., 1992. "An econometric forecasting model of revenues from urban parking facilities," Journal of Economics and Business, Elsevier, vol. 44(2), pages 143-150, May.
    10. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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