Development of Planning-Level Transportation Safety Models using Full Bayesian Semiparametric Additive Techniques

Abstract

Recently, several attempts have been made to develop collision prediction models in which spatial dependency is considered. These models recognize the local nature of spatial data by relaxing the regression analysis assumption that the error terms for each observation are independent. The primary objective of this study is to investigate an alternative technique for capturing the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors. Spatial relationships are incorporated into the full Bayesian semiparametric additive modeling framework through the covariance of the error terms. The secondary objective of this research study is to build on knowledge of comparing the accuracy of full Bayesian models to that of generalized linear and geographically weighted Poisson regression models. The spatial covariates from the full Bayesian semiparametric additive model indicate that collision frequencies in traffic analysis zones are spatially correlated. The results of accuracy comparison indicate that the spatial models perform better than the conventional generalized linear models. However, mixed results are obtained when the FBSA models were compared to the geographically weighted Poisson regression models.

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

“…The more roads that contain more vehicles, the greater the likelihood of collision. Previous studies [21][22][23][24][25][26][27][28][29][30][31][32][33]39] reported similar results. According to Kang [47], bus stops can substantially increase pedestrian volume, as well.…”

“…Dumbaugh and Li [25] explored whether a crash is the product of random errors or whether crashes are affected by the characteristics of the construction environment. Hadayeghi et al [26] developed crash prediction models with geographically weighted Poisson regressions and investigated local spatial variations in the relationship between the number of crashes and potential transportation planning predictors, such as land use, socio-economic and demographic features, traffic volume, road network characteristics, dwelling units, and employment type. Guo et al [27] investigated the determinants of crashes involving cyclists, using a comprehensive list of covariates at the TAZ level in Vancouver, Canada.…”

Incorporating transportation safety into land use planning: Pre-assessment of land use conversion effects on severe crashes in urban China

“…Regarding residential land use, Pulugurtha, et al (2013) reported that areas with higher-density residential (urban residential) land uses present a positive correlation with fatal crashes. Moreover, SCs occur more frequently in TAZs with larger areas devoted to resources and industrial land uses (Hadayeghi, Shalaby, & Persaud, 2010), although these areas are closely associated with fewer pedestrian collisions (Chen & Zhou, 2016). Lee, Yasmin, Eluru, Abdelaty, and Cai (2017) also indicated that land uses that are remote from urban areas are associated with a higher proportion of light truck-involved crashes.…”

Socioeconomic and sociodemographic inequalities and their association with road traffic injuries

“…Recently, crash analyses at a macroscopic level received more attention from the research community. Several studies examined the association of a collection of neighborhood-level factors (e.g., traffic patterns, socio-demographic and socioeconomic variables, land use patterns and weather conditions) with crashes, aggregated according to specific spatial scales (Aguero-Valverde and Jovanis, 2006;Hadayeghi et al, 2010aHadayeghi et al, , 2010bHuang et al, 2010;Lovegrove and Litman, 2008;Pirdavani et al, 2013Pirdavani et al, , 2012Wier et al, 2009). Neighborhood-level crash analyses can provide valuable information that would enable cross-sectional neighborhood comparisons, more accurate identification of neighborhood-specific safety problems, and subsequent implementation of appropriate safety interventions (Huang et al, 2010).…”