Integrating Predictive Analytics and Deep Learning for Vehicle Safety Incident Forecasting

Forecasting vehicle safety incidents is an important step towards improving road safety and reducing accidents. Rule-based systems, which often rely on historical data, can be inflexible and reactive, resulting in high false positive rates and poor accuracy. It presents an integrated predictive system that combines with Deep Learning using CNN and LSTM analysis of multimodal data channels (Traffic images, Weather reports, Vehicle sensor signals). The hybrid architecture incorporates spatial and temporal dynamics, achieving a mean prediction accuracy of 92 % and lowering the false positive rate to 10%. It is important to highlight that results show significant improvements over state-of-the-art systems with 89% precision and 85% recall. The model allows safety management in real-time so that authorities can intervene before an accident happens. By creating a shift from reactive to proactive, the new way of forecasting incidents also makes for improved road safety and ultimately saves lives through timely interventions.