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  Published Paper Details:

  Paper Title

REAL-TIME CRASH PREDICTION USING ADAPTIVE BOOSTING IN MACHINE LEARNING

  Authors

  C.Kalaiarasi,  Prasanth.K.C,  Rakesh Kumar.T,  Sakthivel.N

  Keywords

REAL-TIME CRASH PREDICTION USING ADAPTIVE BOOSTING IN MACHINE LEARNING

  Abstract


In this paper, we are going to look into the project which is going to be an advanced managing site which can be used to manage multiple educational institutions along with extra features like as with the exponentially increasing number of vehicles, road safety is a matter of huge concern. Road accidents kill 1.2 million people every year, It causes loss of lives and economical damage, which is a serious concern which needs to be solved, We have used machine learning algorithms to predict the severity of an accident occurring at a particular location and time. Factors like speed limit, age, weather, vehicle type, light conditions and day of the week have been used as parameters for training the model, We have created a web app for user input and output display and a notification is sent to the police to take preventive measures, The model will run with the input data and predicts the severity of an accident occurring at the respective location of the user, This model will play an important role in the planning and management of traffic and would help us reduce a lot of road accidents in the future.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTS020018

  Paper ID - 223508

  Page Number(s) - 144-149

  Pubished in - Volume 10 | Issue 6 | June 2022

  DOI (Digital Object Identifier) -   

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

  E-ISSN Number - 2320-2882

  Cite this article

  C.Kalaiarasi,  Prasanth.K.C,  Rakesh Kumar.T,  Sakthivel.N,   "REAL-TIME CRASH PREDICTION USING ADAPTIVE BOOSTING IN MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 6, pp.144-149, June 2022, Available at :http://www.ijcrt.org/papers/IJCRTS020018.pdf

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


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