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@article{170587,
        author = {Miss. Seema Y. Somawanshi and Prof. Shah H.P},
        title = {Traffic accident’s severity prediction: a deep learning approach-based CNN network: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1383-1389},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170587},
        abstract = {Predicting the severity of traffic accidents is crucial for improving road safety and developing effective intervention strategies. This review paper explores the application of Convolutional Neural Networks (CNNs) in predicting traffic accident severity. CNNs, with their ability to automatically learn spatial hierarchies of features, have shown promise in handling complex patterns within image and sensor data associated with traffic accidents. The paper provides a comprehensive analysis of various CNN architectures and their efficacy in predicting accident severity. It reviews existing literature, highlighting the strengths and limitations of different CNN-based approaches, and discusses the challenges in integrating CNNs with real-world traffic data. The review also identifies gaps in current research and suggests future directions for improving predictive accuracy and model generalizability. This paper aims to provide a consolidated view of the state-of-the-art in CNN-based traffic accident severity prediction and to guide future research efforts in this field.},
        keywords = {Traffic Accident Severity, Convolutional Neural Networks (CNNs), Deep Learning, Predictive Modeling, Image Analysis, Sensor Data, Traffic Safety, Accident Prediction etc.},
        month = {December},
        }