Accident Alert System for High-Risk Zones: An IoT and Machine Learning Approach with Real-Time Predictive Analytics

Accidents in high-intensity regions demand proactive measures to mitigate risks and enhance public safety. This study proposes an IoT-based Accident Alert System (AAS) that employs machine learning and real-time data analytics to predict accident-prone zones and trigger pre-emptive alerts. The system integrates historical accident data (frequency, severity, environmental factors) with real-time sensor inputs (speed, location, road conditions) to train a predictive model using Support Vector Machines (SVM). Field tests in high-risk urban areas demonstrate a 96 percentage accuracy in identifying accident hotspots, reducing emergency response times by 32 percentage, and lowering accident rates by 18 percentage compared to baseline metrics. The system’s architecture leverages cloud computing for scalability and Raspberry Pi based edge devices for real-time monitoring. Results validate the efficacy of datadriven interventions in accident prevention, offering a replicable framework for smart city deployments