Comparative Study of Machine Learning Algorithm for Traffic Accident Prediction and Prevention

EasyChair Preprint 14611

Abstract

The goal of this research is to compare supervised learning algorithms in rural and urban areas. Road accidents have become a major concern and the main cause of mortality globally. The objective of this research article is to analyse road accidents, estimate the severity of each accident using supervised learning algorithms, and identify the elements that contributed to these accidents. The goal is to solve the issue of safety by creating an accurate prediction model capable of identifying trends in diverse settings and preventing traffic accidents. To achieve this, machine learning algorithms are employed to predict different traffic accident scenarios and identify the most significant factors contributing to these accidents. By utilizing a machine learning model, a cost-effective approach can be developed for implementing safety measures. The ultimate goal of this model is to enhance the accuracy of accident prevention measures and improve overall security. For the analysis, three supervised learning algorithms, namely random forest, decision tree, and SVM, are utilized to predict traffic accident data. These algorithms are chosen due to their ability to provide accurate predictions while considering low-budget scientific measures for accident reduction. In conclusion, this research paper aims is to compare the performance of different supervised machine learning algorithms in rural and urban environments in order to analyze road accidents, determine accident severity, and identify the factors responsible for these accidents. By utilizing machine learning-based models and predictive algorithms, the goal is to develop cost-effective safety measures for preventing traffic accidents and improving overall security. Through comprehensive analysis and evaluation, this research aims to contribute to the field of accident prevention and safety enhancement.

Keyphrases: Decision Tree, Random Forest, machine learning

@booklet{EasyChair:14611,
  author    = {Manisha Verma and Bharti Sharma},
  title     = {Comparative Study of  Machine  Learning Algorithm  for  Traffic Accident Prediction and Prevention},
  howpublished = {EasyChair Preprint 14611},
  year      = {EasyChair, 2024}}