Abstract
Road safety and accidents have been an important concern for the entire world and everyone is putting effort into resolving the long-standing problem of road safety and accidents. In every country on earth, there is traffic and reckless driving. This has a negative impact on a lot of pedestrians. They become victims, although having done nothing wrong. The number of traffic accidents is rising quickly due to the enormous increase in road cars. Accidents like these result in harm, impairment, and occasionally even fatalities. Numerous things like weather changes, sharp curves, and human error all contribute to the high number of traffic accidents. In this research paper various machine learning techniques such as, K Nearest Neighbors, Random Forest, Logistic Regression, Decision Tree, and XGBoost etc., are used to investigate why road traffic accidents occur in various nations throughout the world. For evaluating and analyzing these algorithm several metrics, including precision, recall, accuracy and F1-Score are used to improve the performance of the dataset and predicts accuracy by approximately more than 85%.
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Acknowledgement
This research is supported by research seed grant under IoE, BHU [grant No. R/Dev/D/IoE/SEED GRANT/2020-21/Scheme No.6031/Dr. Awadesh kumar].
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Gupta, S., Kumar, A. (2024). Machine Learning Strategies for Analyzing Road Traffic Accident. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_35
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DOI: https://doi.org/10.1007/978-3-031-53827-8_35
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