A ROAD ACCIDENT PREDECTION MODEL USING DATA MINING TECHNIQUES

Chinthakindi Vishwaja; Dr.E.Srikanth Reddy; Dr. P. Venkateshwarlu

doi:10.62643/

Authors

Chinthakindi Vishwaja Author
Dr.E.Srikanth Reddy Author
Dr. P. Venkateshwarlu Author

DOI:

https://doi.org/10.62643/

Keywords:

Road Accident Prediction, Data Mining, Decision Tree, Random Forest, Naive Bayes, Traffic Analysis, Accident Prone Areas, Predictive Analytics, Road Safety, Traffic Management.

Abstract

Road accidents are a major concern worldwide, leading to significant loss of life, property, and economic resources. Predicting accident-prone areas and conditions can help authorities implement preventive measures and improve road safety. This project proposes a Road Accident Prediction Model using Data Mining Techniques to analyze historical traffic, weather, and road condition data for identifying patterns and predicting potential accidents. Various data mining algorithms, including Decision Trees, Random Forest, and Naive Bayes, are applied to extract meaningful insights from large datasets. The model considers factors such as traffic density, road type, weather conditions, time of day, and driver behavior to predict the likelihood of accidents. By providing predictive analytics, the system helps in reducing accident risks, improving traffic management, and supporting informed decision-making by authorities.