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The objective of this research is to identify and compare contributing factors to pedestrian injury severities in pedestrian-vehicle crashes considering both time-of-day and day-of-week. The pedestrian-vehicle crash data are collected from 2007 to 2018 in North Carolina with categorical factors of pedestrian, driver, vehicle type, crash group, geography, environment, and traffic control characteristics. The final dataset includes 17,904 observations with 69 categorized variables. Four mixed logit models are developed to analyze the crash dataset with segmentations of weekday daytime, weekday nighttime, weekend daytime, and weekend nighttime. A total number of 31 fixed significant factors and 6 random parameter factors to the pedestrian injury severity are detected in four mixed logit models. According to marginal effects, large vehicle involved, pedestrians with age over 65, hit and run, drunk pedestrian, down/dusk light, dark without roadside light, and industrial land use are identified as the contributing factors that result in more than a 0.08 increase in the probability of fatal injury. Compared to the daytime, most factors are found to have more impact on severe injuries in the nighttime. Also, most factors are found to result in more severe injuries on weekends than on weekdays. This study identifies and compares the factors to pedestrian injury severity in pedestrian-vehicle crashes considering the temporal variance in time-of-day (i.e., daytime vs. nighttime) and day-of-week (i.e., weekdays vs. weekends). Random effects are explored in mixed logit models. Differences and possible reasons for the significant factors’ impact within and across time-of-day and day-of-week are also investigated. Corresponding countermeasures and suggestions to mitigate the impacts of major factors are also discussed, which give practical guidance to planners and engineers, and provide a solid reference to further explore the temporal variance of the crash data.

Cytomegalovirus (CMV) is the most common cause of congenital infection, affecting 1% of all live births. Intrauterine infection such as CMV infection is a risk factor for developing cerebral palsy. This study aims to investigate the association between congenital CMV infection and the development of cerebral palsy. A systematic literature search was conducted in PubMed, Web of Science and Ovid SP to identify relevant studies. The quality of studies was assessed using the Newcastle-Ottawa Scale. The random-effect model was used to calculate the pooled prevalence. The generic inverse variance method was used for statistical analysis. A total of 12 studies were included in this systematic review and meta-analysis. The overall pooled prevalence of cerebral palsy among patients diagnosed with congenital CMV infection was 26% (95% confidence interval (CI), 13-40%). The overall pooled prevalence of congenital CMV infection among patients with cerebral palsy was 10.9% (95% CI, 5-16%). Congenital CMV infection was significantly associated with the development of cerebral palsy in children. Routine follow-ups should be offered to screen for cerebral palsy.

Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.

Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.

There are more than 2000 pedestrians reported to be involved in traffic crashes with vehicles in North Carolina every year. 10%–20% of them are killed or severely injured. Research studies need to be conducted in order to identify the contributing factors and develop countermeasures to improve safety for pedestrians. However, due to the heterogeneity inherent in crash data, which arises from unobservable factors that are not reported by law enforcement agencies and/or cannot be collected from state crash records, it is not easy to identify and evaluate factors that affect the injury severity of pedestrians in such crashes. By taking advantage of the latent class clustering (LCC), this research firstly applies the LCC approach to identify the latent classes and classify the crashes with different distribution characteristics of contributing factors to the pedestrian-vehicle crashes. By considering the inherent ordered nature of the traffic crash severity data, a partial proportional odds (PPO) model is then developed and utilized to explore the major factors that significantly affect the pedestrian injury severities resulting from pedestrian-vehicle crashes for each latent class previously obtained in the LCC. This study uses police reported pedestrian crash data collected from 2007 to 2014 in North Carolina, containing a variety of features of motorist, pedestrian, environmental, roadway characteristics. Parameter estimates and associated marginal effects are mainly used to interpret the models and evaluate the significance of each independent variable. Lastly, policy recommendations are made and future research directions are also given.

Problem: The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes. Methods: The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects. Results: Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes.

This study investigates factors that significantly contribute to the severity of driver injuries resulting from single-vehicle run-off-road (SV ROR) crashes. A mixed logit model approach is employed to explore the potential unobserved heterogeneous effects associated with each age group: young (ages 16-24), middle-aged (ages 25-65), and older drivers (ages over 65). Likelihood ratio tests indicated that the development of separate injury severity models for each age group is statistically superior to estimating a single model using all data. Based on the crash data collected from 2009 to 2013 in North Carolina, a series of driver, vehicle, roadway, and environmental characteristics are examined. Both parameter estimates and their elasticities are developed and used to interpret the models. The estimation results show that contributing factors which significantly affect the injury severity of an SV ROR crash differ across three age groups. Use of restraint device and horizontal curves are found to affect crash injuries and fatalities in all age groups. Reckless driving, speeding, distraction, inexperience, drug or alcohol involvement, presence of passengers, and driving an SUV or a van are found to have a more pronounced influence in young and middle-aged drivers than older drivers. Compared to the passenger cars, older drivers are less likely to experience possible injuries in a large-size vehicle (e.g., truck or bus). The average annual daily traffic volume and lighting conditions are also found to influence the resulting injury severity of an SV ROR crash specific to young drivers.

Objective: The objective of this research is to identify and compare contributing factors to head-on crashes with drivers under and not under the influence of alcohol or drugs. Methods: The head-on crash data are collected from 2005 to 2013 in North Carolina from four aspects: vehicle, driver, roadway, and environmental characteristics. The final dataset includes 9,153 head-on crashes. A mixed logit model is developed to analyze the crash dataset involving drivers under and not under the influence of alcohol or drugs. Results: According to the obtained results, factors such as rural roadways, adverse weather, curve road, and high speed limit are among the most significant contributing factors to both head-on crashes with DUI and non-DUI. In addition, the results of this research demonstrate that high speed limit is found to be better modeled as random-parameters at specific injury severity levels for head-on crashes with DUI. Besides the factors mentioned above, dark light condition, old drivers, pickups, and motorcycles also significantly affect the severity of head-on crashes with non-DUI. Conclusions: The results of this study identify various factors that significantly affect the severity of head-on crashes with drivers under and not under the influence of alcohol or drugs. Also, the mixed logit model examines the heterogeneous effects and correlation in unobserved factors by allowing coefficients to be randomly distributed. The findings of this study call for more attention to head-on crashes and provide a reference for planners and engineers when developing and selecting countermeasures to reduce and/or mitigate head-on crashes.