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@article{174544,
        author = {V Praveen Raj Kumar and V.Govinda Rao and P.Harish and R.Srinivas and Md.Abdul Zafar},
        title = {Framework for Analyzing Road Accidents and Reporting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {149-154},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174544},
        abstract = {Road accidents pose a persistent challenge to public safety, necessitating robust detection systems to mitigate fatalities and expedite emergency responses. This study presents a detailed comparative analysis of an existing Random Forest model and a proposed Convolutional Neural Network (CNN) model for road accident detection. The Random Forest model leverages structured data, including weather conditions, vehicle speed, and road types, to predict accidents with reasonable accuracy, yet it struggles to interpret visual patterns in dynamic traffic environments. In contrast, the proposed CNN model harnesses traffic camera imagery to identify spatial features such as vehicle collisions and abnormal movements, offering enhanced detection precision and reduced false positives. By evaluating both models’ strengths and limitations, this research demonstrates the CNN’s superior capability for real-time accident detection, leveraging visual data to complement traditional structured inputs. The findings underscore the potential of CNN-based systems to improve traffic monitoring and public safety outcomes.},
        keywords = {Road accident detection, Random Forest, Convolutional Neural Network (CNN), Machine learning, Deep learning, Traffic monitoring, Public safety, Real-time detection.},
        month = {March},
        }