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This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.

This study intended to provide potential safety consideration that will pave the way for evaluation of connected and autonomous vehicles (CAV) in public buses. The geo-localized crash data of Las Vegas metropolitan area from 2014 to 2017 were collected, involving 27 arterials with 466 bus crash samples, and Chi-square Automatic Interaction Detection (CHAID) decision tree model was proposed to examine the effect of CAV technologies in bus crash severity so that the drivers' factors can be determined and controlled if CAV technologies were employed. Results suggest that contributory predictors of crash severity outcomes are from driver's action of vehicles with main responsibility (including going straight, making U-turn and passing other vehicles/racing), and crash type (angle and rear-end). If these factors are controlled by CAV technologies, it is suggested that severe crashes involving buses could be reduced significantly. The findings provide useful insights for CAV companies and policy makers to improve the driving state and traffic safety in public buses. INDEX TERMS Connected and autonomous vehicles, CHAID decision tree, public bus, crash severity.

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

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