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Highway Truck Parking Prediction System and Statistical Modeling Underlying its Development
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SYSNO ASEP 0442563 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Highway Truck Parking Prediction System and Statistical Modeling Underlying its Development Author(s) Brabec, Marek (UIVT-O) RID, SAI, ORCID
Konár, Ondřej (UIVT-O) RID, SAI, ORCID
Kasanický, Ivan (UIVT-O) RID, ORCID
Pelikán, Emil (UIVT-O) SAI, RID
Malý, Marek (UIVT-O) RID, SAISource Title Proceedings of the Second International Conference on Traffic and Transport Engineering. - Belgrade : City Net Scientific Research Center, 2014 / Čokorilo O. - ISBN 978-86-916153-2-1 Pages s. 164-170 Number of pages 7 s. Publication form Medium - C Action ICTTE 2014. International Conference on Traffic and Transport Engineering /2./ Event date 27.11.2014-28.11.2014 VEvent location Belgrade Country RS - Serbia Event type WRD Language eng - English Country RS - Serbia Keywords highway truck parking ; prediction system ; dynamical statistical modeling ; generalized additive model Subject RIV BB - Applied Statistics, Operational Research R&D Projects TA02031411 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) Institutional support UIVT-O - RVO:67985807 UT WOS 000348569200021 Annotation In this paper, we will describe a system for on-line prediction of truck parking demand along highway system in the Czech Republic. We will describe structure of the system developed during the TACR TA02031411 project and mention some of its specific functionalities. Further, we will explain in detail statistical modeling methodology which underlies the forecasting model in the core of the prediction procedure. 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.). Our 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 are available in the form of times when individual truck pass toll gates). After constructing appropriate proxy variable, we formulate a flexible class of statistical semi-parametric models constructed in a Markovian fashion. In fact, our 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. We will demonstrate practical performance of the prediction system under routine conditions, based on evaluation against manual parking lot counting. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2016
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