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Traffic Accident Prediction Model Using Deep Learning Approach in VANET– a Review

EasyChair Preprint no. 11201

6 pagesDate: October 30, 2023


In today’s era, development in transportation is rising day by day. Vehicular Ad Hoc Network (VANET) is a crucial part of advanced transportation system framework. Number of vehicles is increasing rapidly due to their high need and demand by people. This reason somewhere also leads to increase in number of traffic accidents. Road traffic accident is a very serious threat to human life. It harms the safety of living environment as well. Thus it is essential to provide a prominent solution to this problem. Traffic accident prediction and prevention is an important step for vehicular safety purpose. Various traffic accident prediction techniques using machine learning and deep learning algorithms are being tested by researcher. The study of such techniques is done in this paper. This study may act as a benchmark to use particular method as per one’s available resource. Also, after proper analysis of each of these methods, the scope for further development will increase that helps to create a space for new research.

Keyphrases: deep learning, Traffic Accident Prediction, VANET, vehicle safety

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shweta Shendekar and Samrat Thorat and Dinesh Rojatkar},
  title = {Traffic Accident Prediction Model Using Deep Learning Approach in VANET– a Review},
  howpublished = {EasyChair Preprint no. 11201},

  year = {EasyChair, 2023}}
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