Authors : K. Sai Pragna; Yukta Paranjpe; Suraj Mallapur; Mukesh Marwade; Gayathri K.; Bharani Kumar Depuru; Srija Depuru; Bhargavi Depuru

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/y6evcr3d

Scribd : https://tinyurl.com/4t8pw4f8

DOI : https://doi.org/10.38124/ijisrt/26apr1604

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Road traffic accidents remain a critical challenge due to the complex interaction of traffic conditions, signal operations, and environmental factors. This paper presents a machine-learning-based approach to accident-risk classification using traffic and signal data. The proposed system analyses a structured dataset comprising multiple features related to traffic flow, road conditions, and contextual variables to predict accident-prone scenarios. A comprehensive data preprocessing pipeline is implemented, including handling missing values, categorical encoding, feature scaling, and class imbalance. Multiple classification models are evaluated to identify the most effective approach for risk prediction. The results indicate that ensemble-based models achieve superior performance in capturing complex patterns within the data. The final model is integrated into a deployment-ready framework that enables real-time accident-risk prediction via an interactive interface. The proposed system supports proactive traffic management by identifying high-risk conditions in advance, thereby improving road safety and decision-making.

Keywords : Road Traffic Accidents, Accident Risk Prediction, Machine Learning, Ensemble Learning, Traffic Data Analysis, Intelligent Transportation Systems.

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