Nõudluse ennustamine nutikatele parkimissüsteemidele
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Date
2019
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Abstract
Kiire areng infotehnoloogias on võimaldanud inimestel, kes ei ole antud ala eksperdid, luua täpseid masinõppe mudeleid, mis suudavad õppida olemasolevatest andmetest. Selliste mudelite rakendamine linnaplaneerimisel esinevatele probleemidele võib aidata luua paremaid elamistingimusi suurlinnades. Antud töö keskendub tehisnärvivõrgule, mis suudaks ennustada parkimisala hõivatust. Töö käigus välja töötatud mudeli integreerimine süsteemi, mis suunab inimesi parkimiskohtadele võiks anda väärtusliku toote, mis vähendaks liiklusummikuid ja parkimiskoha otsimiseks kuluvat aega.
The rapid development of information technologies has enabled to create accurate machine learning models that could learn from the existing data without the need of an expert in the field. Applying these models in the field of urban design problems could help to create better living conditions in large cities. This research focuses on creating a neural network that could predict parking area occupancy. Integrating the model to a parking guidance platform would produce a valuable product that could help to reduce the traffic congestions and the cruising time for a parking spot.
The rapid development of information technologies has enabled to create accurate machine learning models that could learn from the existing data without the need of an expert in the field. Applying these models in the field of urban design problems could help to create better living conditions in large cities. This research focuses on creating a neural network that could predict parking area occupancy. Integrating the model to a parking guidance platform would produce a valuable product that could help to reduce the traffic congestions and the cruising time for a parking spot.