Abstract
Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.
Highlights
Road accident is one of the most important ongoing issues in the modern times traffic on roads
WHO has reported top ten disastrous reasons for taking human’s life, and road accidents come at ninth place
This study made an analysis between the classification algorithms where the Random Forest (RF)provides the highest accuracy for crash prediction for datasets obtained from US and India
Summary
Road accident is one of the most important ongoing issues in the modern times traffic on roads. WHO has reported top ten disastrous reasons for taking human’s life, and road accidents come at ninth place. Crash predictor system is used to predict the accidents in the roads with the help of machine learning algorithms [1]. Accidents cause a huge impact on the society, where there is a great cost of casualties and injuries to the people. To avoid those accidents crash predictor systems can be used
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