Abstract

Vehicle accidents are a significant concern in smart cities due to the increasing number of vehicles and the potential impact on traffic flow, safety, and emergency response. To address this issue, this paper proposes a Vehicle Accident Prediction and Detection Model for Smart Cities using Edge Computing. The model uses edge computing, which enables real-time data processing and analysis at the edge of the network, closer to the source of data generation. This approach reduces latency and bandwidth requirements by processing data locally, making it suitable for time-sensitive applications like accident prediction and detection. The proposed model utilizes various data sources such as traffic cameras, sensors embedded in vehicles, and historical accident data. These sources provide real-time information about road conditions, vehicle movements, and past accident patterns. The collected data is processed using machine learning algorithms to identify patterns and predict potential accident-prone areas.The proposed system uses smart city infrastructure such as sensors and high resolution cameras to capture any possible incident and analyze these data for any possible accidents. Any hazard occurrence leads to send a voice alert to the car driver (owner) telling to perform some steps that avoid him many real accident and save his and others life.