Beta Distribution Learning for Reliable Roadway Crash Risk Assessment

2025-11-07

AI Summary

Traditional traffic crash risk assessment suffers from two key limitations: (1) isolated modeling neglects the spatial complexity and contextual interactions of the built environment; and (2) neural network–based approaches yield only point estimates, lacking uncertainty quantification—thereby undermining decision reliability. To address these, we propose a geospatial deep learning framework leveraging satellite imagery that jointly performs spatial feature extraction and Beta-distribution–based probabilistic modeling for fatal crash risk prediction. Our method captures multi-scale spatial patterns and interaction effects within road environments while outputting full predictive probability distributions—enhancing uncertainty awareness, model calibration, and interpretability. Experiments demonstrate that our approach improves high-risk segment recall by 17–23% over baseline models. The resulting probabilistic risk assessments provide a robust, trustworthy foundation for autonomous vehicle safety decision-making and resilient urban planning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Addresses spatial complexity in traffic safety studies using satellite imagery
• Overcomes point estimate limitations with Beta distribution uncertainty modeling
• Provides reliable crash risk assessment for autonomous navigation and urban planning

Innovation

• Geospatial deep learning framework using satellite imagery
• Estimates Beta probability distribution for uncertainty-aware predictions
• Outperforms baselines with improved recall and calibration