Proactive Accident Prevention Using Cloud and IoT Based Driving Behavior Analysis

Abstract: Road accidents continue to be a leading cause of mortality worldwide, primarily driven by human factors such as reckless driving, fatigue, and delayed reaction times. Existing solutions predominantly focus on post-accident detection rather than prevention, limiting their effectiveness in reducing accident rates. This paper proposes a proactive accident prevention framework that integrates Internet of Things (IoT) sensing, cloud computing, and machine learning for real-time driving behaviour analysis and risk prediction. The system continuously collects multi-dimensional data, including vehicle dynamics and driver activity, through embedded sensors such as accelerometers, gyroscopes, GPS, and speed monitors. This data is transmitted to a cloud-based infrastructure using lightweight communication protocols, where it undergoes preprocessing and intelligent analysis. Advanced machine learning models are employed to identify complex patterns associated with unsafe driving behaviours, including aggressive acceleration, abrupt braking, and abnormal steering patterns. A dynamic risk scoring mechanism is developed to quantify accident probability, enabling timely detection of hazardous conditions. Upon identifying high-risk scenarios, the system generates real-time alerts through integrated interfaces, facilitating immediate corrective actions by drivers or fleet managers. Experimental evaluation demonstrates that the proposed system achieves high accuracy in detecting risky driving behaviour and significantly improves proactive safety measures. The proposed framework offers a scalable, cost-effective, and intelligent solution for next-generation transportation systems by shifting the paradigm from reactive accident response to predictive and preventive safety mechanisms.