Single vehicle (SV) run-off-road crashes are a major cause of severe injury and fatality. Such crashes can result in different levels of severity depending on the direction (i.e., left or right) in...

Crashes occur from a combination of factors related to the driver, roadway, and vehicle factors. The impact of vehicles on road crashes is a critical consideration within road safety analysis, even though not much studies have been conducted in this area. This study assessed how various vehicle and other crash factors are significantly associated with crash outcomes. To do this, historical vehicle defect-related crashes were obtained for the state of Alabama from 2016 to 2020. After data cleaning, a crash injury severity model was developed using the random parameters multinomial logit with heterogeneity in means approach to account for possible unobserved heterogeneity in the data. A spatial analysis was further conducted to better understand vehicle defect crashes as a broader societal issue and potentially explore their connection with the socio-demographic characteristics of the drivers of these vehicles. The preliminary data analysis showed that brake and tire defects accounted for about 65% of the vehicle defects associated with the crashes. The model estimation results revealed that improper tread depth and headlight defects were associated with major injury outcomes, while brake defects were more associated with minor injuries. Also, crashes associated with speeding, drunk driving, failure to use seatbelts, and those that occurred on curved roads left with downgrades were likely to result in major injuries. Findings from the spatial analysis showed that postal codes with higher median incomes are more likely to record lower vehicle defect-related crashes, unlike those that have higher proportions of females and African Americans. The study's findings provide data-driven evidence for sustained safety campaigns, workshops, and training on basic vehicle maintenance practices in the low-income communities in the state.

The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016–2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-of-state large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-of-state model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers’ training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.

The COVID-19 travel restriction orders have significantly reduced travel and generally lowered the risk of road traffic collisions, but many accounts suggest an increase in risky driving behaviors and consequent fatal crashes during the shelter-in-place period. Risky driving behaviors including failure to wear a seatbelt, speeding, and drunk driving were observed to be the leading contributing factors of the fatalities. Whereas the fatal crashes that characterized the shelter-in-place period has become a topical issue, the high number of crashes that occurred as a result of the panic shopping and increased travel activities in the weeks before the shelter-in-place order have not received much attention. In this study, we investigated the differences and similarities in the effects of the factors that were associated with crash injury severity before, during, and after the shelter-in-place order. The study used crash data from the state of Alabama for the 2020 calendar year. Preliminary data analysis revealed interesting variations in crash trends across the three periods. It was found that the highest weekly crash frequency occurred in the immediate week before the shelter-in-place order, and a higher proportion of crashes that occurred between 6 p.m. and 6 a.m. and those that occurred in residential areas happened during the shelter-in-place period while shopping area crashes, manufacturing/industrial area crashes, rear-end collisions, and crashes involving female drivers occurred mostly before the shelter-in-place period. Three injury severity models were developed using random parameters logit with heterogeneity in means and variances approach. The results showed that major injury crashes occurred mainly in rural areas and occurred due to speeding, fatigue driving, and failure to use a seatbelt. The effects of these factors on crash outcome did not vary across the year, indicating that the shelter-in-place order did not impact the driving behaviors of the driver population that got into major injury crashes. The results further revealed that the effects of some crash factors, such as road type and manner of collision, varied across the periods. The findings of the study provide a deeper, data-driven understanding of how driving behaviors and associated crash outcomes may be affected by extreme events such as the COVID-19 shelter-in-place.