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[2602.13339] An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features
Summary
This article presents a novel causal inference framework for traffic safety modeling, utilizing semantic features from street-view images to analyze the impact of visual environments on traffic crashes.
Why It Matters
Understanding the causal relationships between visual environmental features and traffic safety is crucial for developing effective policy interventions. This research highlights the importance of greenery in urban areas as a protective factor against traffic crashes, particularly in vulnerable communities.
Key Takeaways
- The study introduces a Double Machine Learning framework to analyze traffic safety.
- Semantic segmentation of street-view images reveals significant causal effects of greenery on traffic crashes.
- Findings indicate spatial heterogeneity in the impact of greenery, especially in urban cores.
- The research emphasizes the need for tailored design optimizations to protect vulnerable road users.
- Causal evidence supports greening as a viable intervention for improving traffic safety.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13339 (cs) [Submitted on 12 Feb 2026] Title:An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features Authors:Lishan Sun, Yujia Cheng, Pengfei Cui, Lei Han, Mohamed Abdel-Aty, Yunhan Zheng, Xingchen Zhang View a PDF of the paper titled An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features, by Lishan Sun and 6 other authors View PDF Abstract:Macroscopic traffic safety modeling aims to identify critical risk factors for regional crashes, thereby informing targeted policy interventions for safety improvement. However, current approaches rely heavily on static sociodemographic and infrastructure metrics, frequently overlooking the impacts from drivers' visual perception of driving environment. Although visual environment features have been found to impact driving and traffic crashes, existing evidence remains largely observational, failing to establish the robust causality for traffic policy evaluation under complex spatial environment. To fill these gaps, we applied semantic segmentation on Google Street View imageries to extract visual environmental features and proposed a Double Machine Learning framework to quantify their causal effects on regional crashes. Meanwhile, we utilized SHAP values to characterize the nonlinear influence mechanisms of confounding variables in the models and applied caus...
