• Tushar Goud
• Pallavi Kumari
• Vedant Patil
• Rishabh Tiwari

Several studies and research show that one of the most frequent causes of death in world is road accidents. Several measures are taken by people and introducing high tech system in vehicles for Safety still deaths and accidents are unavoidable. Road traffic crashes kill approximately 1.19 million people each year. Road traffic injuries are the greatest cause of death among children and young adults aged 5 to 29 years. Reducing the number of events is crucial for saving lives and creating sustainable cities and communities. Machine learning and data analysis tools evaluate the causes of car accidents and recommend ways to reduce them. According to the Global Status Report on Road Safety 2023, 1.19 million less individuals die in traffic accidents each year than in previous years. The research demonstrates that initiatives to improve road safety are having an impact, with considerable decreases in road traffic. This paper provides an overview of traffic accident prediction techniques, including traditional statistical analysis, machine learning, and time series analysis. It also evaluates the benefits of each method. These methods can assess factors impacting traffic accidents, create prediction models, increase accuracy, and help prevention efforts for urban traffic.

Road Accident, Machine learning, Time series analysis.

"Road Accident Severity Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l602-l606, April-2024, Available :http://www.jetir.org/papers/JETIR2404B79.pdf

"Road Accident Severity Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl602-l606, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B79.pdf