Adaptive parking demand prediction using discrete time based dynamic Markov chain

Semeneh Hunachew Bayih and Surafel Lulseged Tilahun

International Journal of Data Analysis Techniques and Strategies, 2025, vol. 17, issue 2, 140-159

Abstract: The demand for urban parking rapidly increases and becomes a significant traffic issue in densely populated metropolitan regions. Prediction of parking demand is crucial for reducing traffic jams and decreasing greenhouse gas emissions. It is also essential to the development of parking facilities and price adjustments in urban parking planning. Most of the earlier studies developed model for parking demand prediction using historical data which lack to update the demand data. Furthermore, the demand predictions are not considering the effect of parking pricing. However, parking pricing affects the demand in a given parking platform. To address this issue, we have considered three categories of parking demand based on price based preference. Dynamic non-homogeneous Markov chain with discrete time and discrete state is used to predict the parking demand. An adaptive approach or a learning approach is proposed to make the Markov chain dynamic and to adapt changes in the demand environment. A numerical example demonstrating the prediction from data collection as well as incorporating the adaptive strategy so that the system learning new changes, is presented.

Keywords: prediction; parking demand; Markov chain model; adaptive learning; discrete time; demand categorisation. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=147517 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:17:y:2025:i:2:p:140-159

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().