Beta Distribution Learning for Reliable Roadway Crash Risk Assessment

• URL: http://arxiv.org/abs/2511.04886v1
• Date: Fri, 07 Nov 2025 00:08:55 GMT
• Title: Beta Distribution Learning for Reliable Roadway Crash Risk Assessment
• Authors: Ahmad Elallaf, Nathan Jacobs, Xinyue Ye, Mei Chen, Gongbo Liang,
• Abstract summary: Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP.<n>Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment.<n>We introduce a novel deep learning framework that leverages satellite imagery as a comprehensive spatial input.<n>This approach enables the model to capture the nuanced spatial patterns and embedded environmental risk factors that contribute to fatal crash risks.
• Score: 21.371420424228077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP. Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment. Furthermore, conventional Neural Network-based risk estimators typically generate point estimates without conveying model uncertainty, limiting their utility in critical decision-making. To address these shortcomings, we introduce a novel geospatial deep learning framework that leverages satellite imagery as a comprehensive spatial input. This approach enables the model to capture the nuanced spatial patterns and embedded environmental risk factors that contribute to fatal crash risks. Rather than producing a single deterministic output, our model estimates a full Beta probability distribution over fatal crash risk, yielding accurate and uncertainty-aware predictions--a critical feature for trustworthy AI in safety-critical applications. Our model outperforms baselines by achieving a 17-23% improvement in recall, a key metric for flagging potential dangers, while delivering superior calibration. By providing reliable and interpretable risk assessments from satellite imagery alone, our method enables safer autonomous navigation and offers a highly scalable tool for urban planners and policymakers to enhance roadway safety equitably and cost-effectively.

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