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In recent years the pedestrian deaths have been declining, but the pedestrian–vehicle death rate in Croatia is still pretty high. This study intended to investigate the injury severity of pedestrian–vehicle crashes and identify the influencing factors. To achieve this goal, the dataset was firstly collected from Traffic Accident Database System maintained by the Ministry of the Interior, Republic of Croatia from 2015 to 2019, and then latent cluster analysis was employed to identify homogenous clusters from heterogeneous dataset. Based on the classified dataset, unbalanced panel mixed ordered probit model was proposed. By analyzing the classes with different vehicles, the proposed model revealed a more complete understanding of significant variables and showed beneficial performance from the goodness-of-fit, while capturing the impact of exogenous variables to vary among different places, as well as accommodating the heterogeneity issue due to unobserved effects. Findings revealed that the proposed model can be considered as an alternative to determine the factors of injury severity and to deal with the heterogeneity issue. The results may provide potential insights for reducing the injury severity of pedestrian-vehicle crashes. [ABSTRACT FROM AUTHOR]

Although the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes. [ABSTRACT FROM AUTHOR]

This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the heterogeneity issue at signalized intersections. To achieve the objectives, Bayesian binary and ordinal quantile regression models were proposed to address the pedestrian injury severity at signalized intersections. The suitability of the proposed method was illustrated with the Hong Kong dataset from 2008 to 2012 and 376 signalized intersections involving 2090 pedestrian-related crashes are selected. It's found that age, injury location, pedestrian special circumstance, pedestrian contributory and the presence of Tram/LRT stops and right turning pocket are significant variables. The results indicated that both Bayesian binary and ordinal quantile regression models not only provide a more comprehensive and in-depth understanding of the relationship between pedestrian injury severity and the explanatory variables, but also highlight the heterogeneity issue for the data collected at different locations and different times without many assumptions. The goodness-of-fit of the proposed models outperforms existing mean models, while the Bayesian binary quantile model provides a better fit than the Bayesian quantile regression for ordinal model. The results can benefit the pedestrian facilities improvement/management and guide a much safer pedestrian environment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• The pedestrian injury severity is investigated by GTWR model. • GWR model and TWR model are integrated to address the spatial and temporal heterogeneity, respectively. • The number of vehicles, number of casualties, speed limit, vehicle movement and injury location have significant influence in different areas. • The findings provide insights for practitioners and policy makers to improve pedestrian safety. This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the spatial and temporal heterogeneity simultaneously. To achieve the objectives, geographically and temporally weighted regression (GTWR) model was proposed to deal with both spatial and temporal heterogeneity simultaneously. The pedestrian crash data of Hong Kong metropolitan area from 2008 to 2012 were collected, involving 1652 pedestrian-related injury samples. By comparing GTWR model and standard geographically weighted regression (GWR) model and temporally weighted regression (TWR) model, the proposed GTWR model showed potential benefits in modeling both spatial and temporal non-stationarity simultaneously in terms of goodness-of-fit and F statistics. Results revealed that number of vehicles, number of pedestrian-related casualties, speed limit, vehicle movement and injury location have significant influence on pedestrian injury severity in different areas. The conclusions are reached that GRWR model can address the relationship between pedestrian injury severities and influencing factors, as well as accommodating spatial and temporal heterogeneity simultaneously. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety. [ABSTRACT FROM AUTHOR]