Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression

In recent years, globally quantile-based model (e.g. quantile regression) and spatially conditional mean models (e.g. geographically weighted regression) have been widely and commonly employed in macro-level safety analysis. The former ones assume that the model coefficients are fixed over space, while the latter ones only represent the entire distribution of variable effects by a single concentrated trend. However, the influence of crash related factors on the distribution of crash frequency is observed to vary over space and across different quantiles. Therefore, a geographically weighted Poisson quantile regression (GWPQR) model is employed to investigate the spatial heterogeneity of variable effects crossing different quantiles. Five categories, including exposure, socio-economic, transportation, network and land use were selected to estimate the spatial effects on crash frequency. In the case study, vehicle related crashes collected in New York City were used to validate the predicted performance of the proposed models. The results show that the GWPQR outperforms the NB, QR and GWNBR for modeling the skewed distribution, reconstructing the crash distribution and capturing the unobserved spatial heterogeneity. Additionally, the significant coefficients are further used to classify all 21 variables into key, important and general parts. Then we discuss how these factors affects the regional crashes over space and distribution of crash frequency. This study confirms that the influencing factors have varying effects on different quantiles of distribution and on different regions, which could be helpful to provide support for making safety countermeasures and policies at urban regional level.