Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity

2017/10/23 by Jun Liu, Asad J. Khattak, Behram Wali · 10 citations
Engineering · Social Sciences · Medicine · #Traffic and Road Safety #Urban Transport and Accessibility #Injury Epidemiology and Prevention

paper · doi:10.1016/j.aap.2017.10.012

Cited by

• The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis
• Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
• Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression
• Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression
• Measuring nonlinear relationships and spatial heterogeneity of influencing factors on traffic crash density using GeoXAI
• Measuring Nonlinear Relationships and Spatial Heterogeneity of Influencing Factors on Traffic Crash Density Using GeoXAI
• Predicting and Interpreting Spatial Accidents through MDLSTM. [europepmc]
• Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway. [europepmc]
• Assessing the effectiveness of built environment-based safety measures in urban and rural areas for reducing the non-motorist crashes. [europepmc]
• Examining spatial heterogeneity in built environment and climate factors affecting motorcycle crashes. [europepmc]