Optimizing Road Accident Ambulance Response Time with Deep Contextual Analysis

Road accidents remain a significant issue in public health arising from the high number of casualties and fatalities. This study proposes a deep learning-based approach to optimize ambulance positioning, aiming to reduce response times and improve emergency care. By using a Cat2Vec model, the framework captures contextual and spatial patterns influencing road crashes. The system is when evaluated against classical clustering algorithms like K-means, GMM, and Agglomerative Clustering. A novel scoring function is introduced to evaluate response time and distance in real time. The proposed model achieves 95% accuracy with k-fold cross-validation and outperforms traditional methods, demonstrating its effectiveness for real-world deployment.