International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning Techniques for Road Accident Severity Prediction: A Review |
|---|---|
| ABSTRACT | Road traffic accidents remain a major global challenge, causing significant fatalities, injuries, and economic losses each year. Predicting accident severity is essential for improving emergency response, traffic management, and road safety planning. With the availability of large-scale accident datasets, machine learning techniques have been widely adopted for severity prediction. This paper presents a comprehensive review of machine learning approaches used for road accident severity prediction. Various models, commonly used datasets, evaluation metrics, and data imbalance handling techniques are analyzed. A comparative discussion highlights the strengths, limitations, and performance trends of existing studies. Finally, key research gaps and future directions are identified to support reliable real-world application |
| AUTHOR | MOHAK NALE, KUNAL PATIL, MAYANK PATIL, PRANAV SHINDE, PRADNYA KASTURE Student, Department of Computer Engineering, RMD Sinhgad School of Engineering, Warje, Pune, Maharashtra, India Assistant Professor, Department of Computer Engineering, RMD Sinhgad School of Engineering, Warje, Pune, Maharashtra, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401081 |
| pdf/81_Machine Learning Techniques for Road Accident Severity Prediction A Review.pdf | |
| KEYWORDS | |
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