Highway Truck Parking Prediction System and Statistical Modeling Underlying its Development

In this paper, the authors describe a system for on-line prediction of truck parking demand along a highway system in the Czech Republic. They describe the structure of the system developed during the TACR TA02031411 project and mention some of its specific functionalities. Further, the authors explain in detail statistical modeling methodology which underlies the forecasting model in the core of the prediction procedure. The whole system relies on the use of indirect but very precise and relatively cheap to obtain toll transaction data (accessible through a cooperation with Kapsch Telematic Services, Inc.). The statistical modeling starts with a recognition of the fact that the number of trucks parking at a given lot and given time is a latent variable to be estimated from the observable toll transaction data (which is available in the form of times when individual truck pass toll gates). After constructing an appropriate proxy variable, the authors formulate a flexible class of statistical semi-parametric models constructed in a Markovian fashion. In fact, the model can be viewed as a non-homogeneous Markov chain, whose Poissonian transition probabilities change with several external covariates (describing e.g. weekly and daily periodicity of parking intensities) as well as spatially. Once the model is estimated (its parametric and nonparametric parts are estimated simultaneously), it is used for real time prediction for several short to medium horizons, using Monte Carlo simulations to obtain efficient and robust software implementation. The authors demonstrate practical performance of the prediction system under routine conditions, based on evaluation against manual parking lot counting.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: pp 164-170
  • Monograph Title: Proceedings of Second International Conference on Traffic and Transport Engineering (ICTTE)

Subject/Index Terms

Filing Info

  • Accession Number: 01600706
  • Record Type: Publication
  • ISBN: 9788691615321
  • Files: TRIS
  • Created Date: May 21 2016 7:51PM