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Abstract
Given the high number of road accidents that result in deaths every day, victims may lose their lives if emergency medical personnel or concerned parties fail to arrive promptly to intervene in an accident or if they do so, because they are unaware of the location of a nearby hospital or medical facility, they may not be able to provide the much-needed help. To avert this scenario, this research work seeks to deploy a Deep Learning Road Accident Emergencies Management model, which by examining of video and sensors’ data, identifies and reports traffic incidents in real time. A Neural Network (NN) is used by the model to recognize accident scenarios based on anomalous motion patterns inside video frames, including sudden halts and crashes. Put into practice in python, the program incorporates deep learning libraries such as OpenCV and TensorFlow for strong data processing and analysis in real time. To train and evaluate the model, an 80/20 data split, which results in reduced latency and high accuracy in accident detection. The Key performance metrics include a testing accuracy of 96%, an F1-score of 87.5%, and a response time of less than two seconds on average. The study advances the domains of artificial intelligence, transportation safety, and emergency management by offering a practical, real-time solution for automated accident reporting. This quick detection capability allows for timely emergency alerts, which are sent to responders via a web-based interface, giving them precise location details. The model's promise as a life-saving tool in smart city infrastructures is highlighted by its capacity to deliver precise and prompt replies. The Road Accident Emergency Reporting Model has important ramifications for increasing road safety, decreasing emergency response times, and boosting public safety in general due to its strong design and low latency.
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References
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