Citation

Soroori E, Mohammadzadeh Moghaddam A, Salehi M. J. Transp. Saf. Secur. 2021; 13(9): 1000-1024.

Copyright

(Copyright © 2021, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2020.1712671

PMID

unavailable

Abstract

There is a wide body of research, especially in developed countries case studies, conducted to quantify safety impacts of transportation strategies. These models can be broadly divided into global and local types. Contrary to consistency assumption of global parameters, parameters' variation over the space, which stems from heterogeneity, can be considered by the local models. Geographically weighted regression (GWR) is a proper technique which is employed to locally estimate the parameters. Geographically weighted Poisson regression (GWPR) is one of the most prevailing count models to be exercised in transportation safety planning. Overdispersion as another consequence of heterogeneity, however, is not addressed by GWPR. Therefore, this study is centered on three targets. First, crash models are developed in a developing country case study. Second, overdispersed and spatial nonstationary data is scrutinized by applying and investigating the performance of geographically weighted negative binomial regression with two aspects of global and spatially varying overdispersion parameters (i.e., GWNBRg and GWNBR). Also, the explanatory performance and prediction accuracy are compared with GWPR, traditional Poisson and negative binomial regressions as two widely used classes of the global models. Finally, the fluctuating safety effects of spatial-unit-related variables at the macrolevel are examined. To this end, geographically coded data on injury crashes, road characteristics, socioeconomic and demographic factors, public transit and land use were collected for 253 traffic zones as the macrolevel spatial units from Mashhad city. The findings indicated that GWR family outperforms traditional regressions in capturing spatial heterogeneity when goodness of fit criteria is compared. Also, GWNBR revealed better performance in terms of AICc and lesser sensitivity to extreme values by developing smoother kernel function of geographical weights. Based on our findings, however, GWNBRs cannot predict other data sets as well as GWPR and researchers are recommended to use GWPR rather than GWNBR. The results also demonstrated that major arterials, ramps, number of intersections, bus stops and employees as well as commercial, residential, and agricultural land uses have considerable impact on injury crashes. In addition, implications regarding the detection of multicollinearity among explanatory variables based on condition index were noted.

Keywords

geographically weighted negative binomial regression; geographically weighted Poisson regression; macrolevel safety analysis; overdispersion; spatial nonstationary; traditional models; transportation safety planning