Road Accident Prediction

Soham Raj Jain

doi:10.32628/IJSRSET2411446

Authors

Soham Raj Jain Kalinga Institute of Industrial Technology, Bhubaneswar, India Author

DOI:

https://doi.org/10.32628/IJSRSET2411446

Keywords:

Road Safety, Machine Learning, Accident Pre- diction, Random Forest, Feature Engineering

Abstract

Road accidents are a global concern, leading to a significant loss of life and property. This paper presents a machine learning-based approach for predicting road accidents by leveraging feature engineering techniques and advanced Ran- dom Forest models. Using enriched datasets with preprocessing methods, the proposed model achieves a predictive accuracy of over 92%. This study also explores key insights from accident data, providing actionable outcomes to improve road safety measures.

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References

A. N. Noyce, S. L. McKnight, and R. W. Kyte, ”Developing a Traf- fic Accident Prediction Model Using Machine Learning Techniques,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2673, no. 8, pp. 35-44, 2019.

J. R. W. Walker, ”Predicting Accident Severity Using Machine Learning Models,” Journal of Safety Research, vol. 71, pp. 42-49, 2019.

G. F. D. R. Road Safety: A Statistical and Machine Learning Perspective, Transportation Research Part C: Emerging Technologies, vol. 98, pp. 23- 39, 2019.

L. J. Mathew and M. B. N. S. Chittaranjan, ”Accident Prediction using Machine Learning Algorithms: A Case Study,” IEEE Intelligent Transportation Systems Conference, 2020, pp. 1458-1463.

R. K. Gupta, ”Predicting Traffic Accidents Using Machine Learning Algorithms,” International Journal of Computer Applications, vol. 179, no. 3, pp. 23-29, 2021.

”Global Status Report on Road Safety,” World Health Organization, 2018.

R. Pressman, Software Engineering: A Practitioner’s Approach, 8th ed. McGraw-Hill, 2018.