Causal and Spatial Safety Modeling

Sun, 26 Apr 2026 00:00:00 +0000

This project line focuses on the mechanisms behind crash risk rather than prediction alone. It uses causal mediation, double machine learning, causal forests, Bayesian hierarchical models, random-parameter models, and spatial effects to explain how roadway, traffic, visual, and behavioral factors shape safety outcomes.

Representative publications include work in Transportation Research Part C, Analytic Methods in Accident Research, Accident Analysis and Prevention, and IEEE Transactions on Intelligent Transportation Systems.

Grouped random parameters Poisson-Lindley model with spatial effects addressing crashes at intersections

Tue, 01 Jul 2025 00:00:00 +0000

This paper is part of the spatiotemporal heterogeneity and Bayesian/statistical safety modeling theme. It studies how intersection crash patterns vary with visual environment features and spatial structure.

Spatial Safety | Chenzhu Wang

Causal and Spatial Safety Modeling

Grouped random parameters Poisson-Lindley model with spatial effects addressing crashes at intersections