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http://hdl.handle.net/20.500.12188/9483
Наслов: | Parking Availability Prediction Using Traffic Data Services |
Authors: | Klandev, Ivan Tolevska, Marta Mishev, Kostadin Trajanov, Dimitar |
Keywords: | Public parking, Parking prediction, Smart city, Smart parking, Traffic congestion, Garage availability, Regressive model, Machine learning |
Issue Date: | 24-сеп-2020 |
Series/Report no.: | ISSN 1857-7288; |
Conference: | ICT Innovations 2020 |
Abstract: | Implementation of a smart parking system providing predictions about real-time parking occupancy is considered to be crucial when managing limited parking resources. In this study, we present a methodology based on machine-learning regression models for predicting parking availability. We use traffic congestion information and garage occupancy as input to the model gathered from public services, and we predict the parking availability in the same garage sixty minutes later. When using the XGBoost regression model, we achieve MSE=0.0567 which confirms the efficiency of our methodology. Additionally, we find that the times- tamp and the current parking availability value are the most influencing factors in prediction which proves the auto-regressive nature of the observed problem. |
URI: | http://hdl.handle.net/20.500.12188/9483 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
Files in This Item:
File | Опис | Size | Format | |
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parking-availability-prediction-using-traffic--data-services.pdf | 1.06 MB | Adobe PDF | View/Open |
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