Injury severity analysis of time-of-day fluctuations and temporal volatility in reverse sideswipe collisions: A random parameter model with heterogeneous means and heteroscedastic variances.

Problem: Sideswipe collisions in the opposite direction often result in more severe injuries than the typical same-direction crashes, especially when light trucks are involved. This study investigates the time-of-day fluctuations and temporal volatility of potential factors that affect the injury severity of reverse sideswipe collisions.

Methods: A series of random parameters logit models with heterogeneous means and heteroscedastic variances are developed and utilized to explore unobserved heterogeneity inherent in variables and preclude biased parameter estimation.

Modeling pedestrian injury severity in pedestrian-vehicle crashes considering different land use patterns: Mixed logit approach.

Objective: The objective of this study is to identify and compare the contributing factors to pedestrian injury severity in pedestrian-vehicle crashes considering different land use patterns.

Methods: The pedestrian-vehicle crash data from 2007 to 2018 were collected from the North Carolina Department of Transportation (NCDOT). A total number of 15,807 observations with 72 categorical variables were included in the final dataset.

Injury severity analysis of rollover crashes for passenger cars and light trucks considering temporal stability: A random parameters logit approach with heterogeneity in mean and variance.

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.

Mixed logit approach to analyzing pedestrian injury severity in pedestrian-vehicle crashes in North Carolina: Considering time-of-day and day-of-week.

Objective: 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.

Methods: 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.

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach.

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.

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models.

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 301 Classification: A Proposed Modification to the Stanford Type B Aortic Dissection Classification for Thoracic Endovascular Aortic Repair Prognostication.

Objective: To assess the usefulness of a modified Stanford classification for risk stratification of complications after thoracic endovascular aortic repair (TEVAR) for type B aortic dissection (TBAD).

Patients And Methods: This retrospective analysis included 201 patients from an observational multicenter cohort study who underwent TEVAR for TBAD from January 1, 2011, to December 31, 2016. The patients were divided by using a modified Stanford classification, termed 301, into 3 groups: types B1 (n=62) and B3 (n=24), with a true and false lumen, respectively, descending closely along the thoracic vertebral bodies, and type B2 (n=115), a semi-spiral or spiral configuration.

Modeling head-on crash severity with drivers under the influence of alcohol or drugs (DUI) and non-DUI.

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. 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.

Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina.

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.

Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference.

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.