Open Access

Accident Risk Prediction Using Artificial Intelligence-Based Intelligent Transportation Systems

https://doi.org/10.55640/irjaet-v03i07-04

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Dr. Puneet Garg ¹ ,

⁴ Associate Professor, Department of CSE, AIKIET Group of Institutions, Delhi NCR, Ghaziabad, India

Abstract

The necessity for intelligent accident risk prediction systems is highlighted by the fact that road accidents continue to be one of the top sources of deaths and financial losses globally. Within the scope of Intelligent Transportation Systems (ITS), this research presents a framework for accident risk prediction to enhance road safety via fast and accurate prediction. The suggested method makes use of the massive US Accidents dataset, which keeps track of millions of actual traffic accident records. For better data quality and predictive performance, extensive data preparation, feature engineering, and feature selection approaches are used. The CatBoost classifier is used due to its proficiency in handling categorical information, its capacity to lessen prediction bias, and its ability to minimize overfitting. The recommended model is evaluated using performance measures including Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix. Experimental results demonstrate that the proposed model obtains a ROC-AUC of 0.94, 92.6% accuracy (ACC), 92.3% precision (PRE), 92.1% recall (REC), and 92.8% F1-score (F1), putting it ahead of other existing machine learning models. Feature significance analysis goes a step further by determining which road and environmental features have the greatest impact on accident risk. Applications involving proactive road safety and intelligent traffic management might benefit from the suggested framework's dependable and efficient solution.

Keywords

Traffic Accident Prediction, Intelligent Transportation Systems (ITS), Machine Learning

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Accident Risk Prediction Using Artificial Intelligence-Based Intelligent Transportation Systems

Abstract

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