Predictive Crash Analytics for Traffic Safety using Deep Learning

• URL: http://arxiv.org/abs/2502.05777v1
• Date: Sun, 09 Feb 2025 05:00:46 GMT
• Title: Predictive Crash Analytics for Traffic Safety using Deep Learning
• Authors: Karthik Sivakoti,
• Abstract summary: This research presents an innovative approach to traffic safety analysis through the integration of ensemble learning methods and multi-modal data fusion. Our primary contribution lies in developing a hierarchical severity classification system that combines spatial-temporal crash patterns with environmental conditions. 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.
• Score: 0.0
• License:
• 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.

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