Get Road Accident Analysis Using Machine Learning

Regression analysis (such as linear regression models, negative binomial regression models and Poisson regression models) has been the most popular technique in crash analysis because the connection between accidents and factors affecting them can be evidently identified.

Explanation: It is important to compute accident rates because it reflects accident involvement by type of highway. These rates compare the safety of different highway and street systems and traffic controls.

The performance of the model is assessed using various evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that the Random Forest Classifier algorithm can effectively predict the risk of traffic accidents with a high degree of accuracy.

Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes.

This project aims at using Machine Learning classification techniques to predict the severity of an accident at any particular location. Machine Learning has enabled us to analyze meaningful data to provide solutions with greater accuracy than humans.

The purpose of accident analysis is to prevent future accidents. In order to do that, the accident analysis method should meet the six criteria, mentioned above. None of the existing methods does demonstrably meet all criteria. Partly this is because evaluative research is lacking.

The control chart technique is one statistical method for doing so, Specifically, it is a pair of accident values having a selected probability of bracl(eting the value of accident expectation.

Accident analysis is a process carried out in order to determine the cause or causes of an accident (that can result in single or multiple outcomes) so as to prevent further accidents of a similar kind. It is part of accident investigation or incident investigation .