Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data: A spatial machine learning approach

2025/07/30 by Lei Han, Mohamed Abdel-Aty, Mohamed Abdel‐Aty · 3 citations
Engineering · #Traffic and Road Safety #Autonomous Vehicle Technology and Safety #Traffic Prediction and Management Techniques

paper · doi:10.1016/j.aap.2025.108180

Citations

• Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression
• Assessing the impact of traffic signal performance on crash frequency for signalized intersections along urban arterials: A random parameter modeling approach
• A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling
• The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis
• Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas
• Changes in traffic crash patterns: Before and after the outbreak of COVID-19 in Florida
• Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
• Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety

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• Segment level safety analysis using lane-changing behavior and driving volatility features from connected vehicle trajectories. [europepmc]