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  Published Paper Details:

  Paper Title

VALET BASED CAR PARKING USING MACHINE LEARNING

  Authors

  Shraddha Thorve,  Swapnali satpute,  Pranjali Salunke,  Sayali Kedar

  Keywords

ar parking, valet, machine learning, k nearest neighbor, android, java, etc

  Abstract


Valet parking system consists of thoughtfully crafted yet easily mastered software applications which are easy to use for technologically less oriented people to use ride on demand service. Our findings have made us understand the need of on demand valet service system in the metropolitan cities of India due to the increasing popula- tion and the subsequent increase in the vehicle traffic. This project introduces a novel algorithm that provides a valet parking system and develops a architecture based on the Fire base Cloud Messaging(FCM) technology. This paper proposed a system that helps users find parking solutions at the least cost based on new performance metrics to calculate the user parking cost by considering the distance and time. This cost will be used to offer a solution of finding an available valet service provide upon a request by the user and a solution of suggesting a new valet service provider if the current valet service provider does not accept the request or nobody is available to serve at the moment. The simulation results show that the algorithm helps in provid- ing on demand valet service to the users and minimizes the users hassle is finding parking in the real time. We will also successfully implement the proposed system in the real world

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2105327

  Paper ID - 206859

  Page Number(s) - d1-d6

  Pubished in - Volume 9 | Issue 5 | May 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Shraddha Thorve,  Swapnali satpute,  Pranjali Salunke,  Sayali Kedar,   "VALET BASED CAR PARKING USING MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 5, pp.d1-d6, May 2021, Available at :http://www.ijcrt.org/papers/IJCRT2105327.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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