Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors

Bayesian spatial-temporal model for the main and interaction effects of roadway and weather characteristics on freeway crash incidence

This study attempts to examine the main and interaction effects of roadway and weather conditions on crash incidence, using the comprehensive crash, traffic and weather data from the Kaiyang Freeway in China in 2014. The dependent variable is monthly crash count on a roadway segment (with homogeneous horizontal and vertical profiles). A Bayesian spatio-temporal model is proposed to measure the association between crash frequency and possible risk factors including traffic composition, presence of curve and slope, weather conditions, and their interactions. The proposed model can also accommodate the unstructured random effect, and spatio-temporal correlation and interactions. Results of parameter estimation indicate that the interactions between wind speed and slope, between precipitation and curve, and between visibility and slope are significantly correlated to the increase in the freeway crash risk, while the interaction between precipitation and slope is significantly correlated to the reduction in the freeway crash risk, respectively. These findings are indicative of the design and implementation of real-time traffic management and control measures, e.g. variable message sign, that could mitigate the crash risk under the adverse weather conditions.

The effect of ethyl alcohol on the severity of injuries in fatal pedestrian victims of traffic crashes

Introduction

A substantial percentage of traffic crashes involve people under the influence of ethyl alcohol. In such circumstances, we speak of the possible effect of ethanol upon trauma outcomes. The present research aimed to assess the state of sobriety fatal pedestrian victims and the correlation between the level of sobriety and the severity of injuries.

Research material and method

The data was obtained from the Warsaw Medical University’s Department of Forensic Medicine. The analysis covered the data for the period of 2009–2013; it encompassed 158 fatal pedestrian victims hit by passenger cars. The appropriate methods of statistical analysis were applied.

Results

The majority of the fatal pedestrian victims were individuals under the influence of ethyl alcohol (72.15%). Significant correlations were observed between the concentration of ethyl alcohol and the victims’ gender (p<0.0001) and age (p = 0.0026). The analysis showed that pedestrians under the influence of ethyl alcohol more often died on the scene (78.95%).

Conclusions

Pedestrians under the influence of ethyl alcohol are a significant group of victims of traffic crashes. Ethyl alcohol is not an independent factor affecting the severity of injuries. A higher percentage of pedestrian victims die on the scene, especially in rural areas.

Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes

The single-vehicle crash has been recognized as a critical crash type due to its high fatality rate. In this study, a two-year crash dataset including all single-vehicle crashes in New Mexico is adopted to analyze the impact of contributing factors on driver injury severity. In order to capture the across-class heterogeneous effects, a latent class approach is designed to classify the whole dataset by maximizing the homogeneous effects within each cluster. The mixed logit model is subsequently developed on each cluster to account for the within-class unobserved heterogeneity and to further analyze the dataset. According to the estimation results, several variables including overturn, fixed object, and snowing, are found to be normally distributed in the observations in the overall sample, indicating there exist some heterogeneous effects in the dataset. Some fixed parameters, including rural, wet, overtaking, seatbelt used, 65 years old or older, etc., are also found to significantly influence driver injury severity. This study provides an insightful understanding of the impacts of these variables on driver injury severity in single-vehicle crashes, and a beneficial reference for developing effective countermeasures and strategies for mitigating driver injury severity.

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

The existing studies on drivers' injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers' injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.