Predictive Crash Analytics for Traffic Safety using Deep Learning

Abstract

Traditional automated crash analysis systems heavily rely on static statistical models and historical data, requiring significant manual interpretation and lacking real-time predictive capabilities. This research presents an innovative approach to traffic safety analysis through the integration of ensemble learning methods and multi-modal data fusion for real-time crash risk assessment and prediction. Our primary contribution lies in developing a hierarchical severity classification system that combines spatial-temporal crash patterns with environmental conditions, achieving significant improvements over traditional statistical approaches. The system demonstrates a Mean Average Precision (mAP) of 0.893, representing a 15% improvement over current state-of-the-art methods (baseline mAP: 0.776). We introduce a novel feature engineering technique that integrates crash location data with incident reports and weather conditions, achieving 92.4% accuracy in risk prediction and 89.7% precision in hotspot identification. Through extensive validation using 500,000 initial crash records filtered to 59,496 high-quality samples, our solution shows marked improvements in both prediction accuracy and computational efficiency. Key innovations include a robust data cleaning pipeline, adaptive feature generation, and a scalable real-time prediction system capable of handling peak loads of 1,000 concurrent requests while maintaining sub-100ms response times.