According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio in order to explore the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. The model results indicate that older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), struck by truck, dark-unlighted roadways, six-lane roadways, and speed limits of 40 mph and 50 mph were all factors associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 a.m. to 3:59 p.m.), weekdays, and daylight condition were all factors associated with less severe injuries. This study provides specific safety recommendations so that effective countermeasures can be developed and implemented by policy makers, which in turn will improve overall highway safety.

This study investigates factors contributing to the injury severity of truck-involved work zones crashes in South Carolina (SC). The outcome of interest is injury or property damage only crashes, and the explanatory factors examined include the occupant, vehicle, collision, roadway, temporal, and environmental characteristics. Two mixed (random parameter) logit models are developed, one for non-interstates with speed limits less than 60 miles per hour (mph) and one for interstates with speed limits greater than or equal to 60 mph, using South Carolina statewide truck-involved work zone crash data from 2014 to 2020. Results of log-likelihood ratio tests indicate that separate speed models are warranted. The factors that were found to contribute to injury at the 90% confidence level in both models (interstate and non-interstate) are (1) dark lighting conditions, (2) female (at-fault) drivers, and (3) driving too fast for roadway conditions. Significant factors that apply only to non-interstates are SC or US primary roadways, activity area of the work zone, at-fault drivers under 35, sideswipe collision, presence of workers in the work zone, and collision with fixed objects. Significant factors that apply only to interstates are three or more vehicles, rear-end collision, location before the first work zone sign, and weekdays.

This study examines factors that contributed to truck-involved work zone crash injury in South Carolina, a state which experienced a rise in the number of injury crashes from 2014 to 2018. The outcome of interest is injury or property damage only (PDO) crashes. A binary logit model is developed using South Carolina statewide work zone crash data from 2014 to 2018. It considers various factors, including vehicle, crash, roadway, environment, day and time, and driver-related characteristics. Of particular interest to this study is the temporal stability of the contributing factors from year to year so that a long-term mitigating strategy can be developed. To this end, the test for parameter transferability is used, and it confirms that separate models should be used for each year, except for 2014. The only factor found to be temporally stable across all years (i.e., statistically significant in 2015–2018 models) is airbag deployment. Given that nearly all the factors are temporally unstable, an importance measure is introduced to enable transportation agencies to rank factors and develop countermeasures that target persistent contributing factors. Based on the importance measure, the top three ranked factors that contributed to work zone truck-involved crash injury in South Carolina from 2015 to 2018 are: (i) airbags deployed, (ii) tie between rear-end crashes and crashes on primary roadways, and (iii) crashes in dark conditions.