Understanding and quantifying the effects of risk factors on crash frequency is of great importance for developing cost-effective safety countermeasures. In this paper, the effects of key crash contributing factors on total crashes and crashes of different collision types are analyzed separately and compared. A novel Machine Learning (ML) method, Light Gradient Boosting Machine (LightGBM), is introduced to model a Texas dataset consisting of vehicle crashes occurred from 2015 to 2017. Compared with other commonly used ML methods such as eXtreme Gradient Boosting (XGBoost), LightGBM performs significantly better in terms of mean absolute error (MAE) and root mean squared error (RMSE). In addition, the SHapley Additive explanation (SHAP) approach is employed to interpret the LightGBM outputs. Significant risk factors are identified, including speed limits, area type, number of lanes, roadway functional class, shoulder width and shoulder type. With the SHAP method, the importance, total effects, and main and interaction effects of risk factors are quantified. The results suggest that the importance of risk factors vary across collision types. Speed limit is a more important risk factor than right/left shoulder width, lane width, and median width for Rear-End (RE) crashes, while the opposite relationship is found for Run-Off-Road (ROR) crashes. Also, it is found that narrow lanes (8ft to 11ft) increase the risk for all types of crashes (i.e., Total, ROR, and RE) in this study. For road segments with 5 or 6 lanes in both directions combined, a lane width greater than or equal to 12ft may help reduce the risk of all types of crashes. These results have important implications for developing accurate crash modification factors and cost-effective safety countermeasures.Automated Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and acceptance of AVs' driving styles. The present study investigated the effects of the designed driving style of AV (aggressive/defensive) and driver's driving style (aggressive/defensive) on driver's trust, acceptance, and take-over behavior in a fully AV. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve driving scenarios in either an aggressive AV or a defensive AV. Results revealed that driver's trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV's driving style and driver's driving style. General estimating equations were conducted to analyze the relationships between driver's trust, acceptance, and take over frequency. The results showed that the effect of driver's trust in AVs on takeover frequency was mediated by driver's acceptance of AVs. These findings implied that driver's trust and acceptance of AVs could be enhanced when the designed AV's driving style aligned with driver's own driving style, which in turn, reduce undesired take over behavior. However, the "aggressive" AV driving style should be designed carefully considering driver safety.
  The management of traffic injuries is challenging for clinicians. Knowledge about predictors of nonrecovery from traffic injuries may help to improve patient care.
 
  To develop a prediction model for self-reported overall nonrecovery from traffic injuries six months post-collision in adults with incident traffic injuries including post-traumatic headache (PTH).
 
  Inception cohort studies of adults with incident traffic injuries (including PTH) injured in traffic collisions between November 1997 and December 1999 in Saskatchewan, Canada; and between January 2004 and January 2005 in Sweden.
 
  Prediction model development and geographical external validation.
 
  The Saskatchewan cohort (development) was population-based (N=4,162).  https://www.selleckchem.com/products/bay-1217389.html  The Swedish cohort (validation) (N=379) were claimants from two insurance companies covering 20% of cars driven in Sweden in 2004.
 
  All adults injured in traffic collisions who completed a baseline questionnaire within 30days of collision. Excluded were those hospitalized>2days, ly=72.6%, 95% CI 61.4%-81.5%; sensitivity=60.5%, 95% CI 53.9%-66.7%); LR+=2.2, 95% CI 1.5-3.3; LR-=0.5, 95% CI 0.4-0.7).
 
  In adults with incident traffic injuries including PTH, predictors other than those related to baseline head and neck pain drive overall nonrecovery. Developing and testing interventions targeted at the modifiable predictors may help to improve outcomes for adults after traffic collision.
 In adults with incident traffic injuries including PTH, predictors other than those related to baseline head and neck pain drive overall nonrecovery. Developing and testing interventions targeted at the modifiable predictors may help to improve outcomes for adults after traffic collision.This study validates the Bayesian hierarchical extreme value model that is developed for estimating crashes from traffic conflicts. The model consists of a generalized extreme value distribution that characterizes the behavior of block maxima extremes and a Bayesian hierarchical structure that incorporates the non-stationarity and unobserved heterogeneity into the extreme analysis. In addition to the block-level factors, the site-level factors are also included in the model development for the first time. The model was applied to data of lane change conflicts collected from 11 basic freeway segments in Guangdong Province, China. Block-level factors such as traffic volume per 10 min, number of lane change events per 10 min, and proportion of oversized vehicles per 10 min and site-level factors such as segment length, curvature, and grade were considered. Two types of Bayesian hierarchical extreme value models were developed, including models without site-level factors and models with site-level factors. These models were also compared to at-site models that were developed for 11 segments separately. The results show that Bayesian hierarchical extreme value models significantly outperform the at-site models in terms of crash estimation accuracy and precision. As well, including site-level factors further improves the model performance in terms of goodness-of-fit. This demonstrates the validity of the Bayesian hierarchical extreme value model. The results also show that number of lane change events, segment length, and grade are significant factors which have adverse effect on the safety of lane changes on freeway segments.