Assessing the effects of geometry and non-geometry related factors in work-zone crashes.

OBJECTIVE: Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. METHOD: This study employed random parameters logit models, accommodating potential variations in parameter estimates' means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. RESULTS: This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories - geometry and non-geometry factors. CONCLUSION: These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes.

Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving in these vehicle categories, shedding light on the variations in single-vehicle crashes. Focusing on single-vehicle crashes in Florida during 2019, involving passenger cars, SUVs, pickup trucks, and minivans caused by distracted driving, the study examines various distractions such as, electronic communication devices (cell phones), electronic devices (navigation systems, music players), internal and external disturbances, texting, and inattentive driving. To analyze the severity of injuries resulting from distracted driving in passenger cars, SUVs, pickup trucks, and minivans, the study employs random parameter multinomial logit models with heterogeneity in means and variances. The model estimates highlight thirty-five significant factors influencing the severity of driver injuries resulting from distracted driving. Notably, the impact of these factors varies significantly depending on the vehicle type (i.e., passenger cars, SUVs, pickup trucks, and minivans). While many explanatory variables are specific to each vehicle type, only one factor (restraint belt usage) is common across all vehicle types, with varying magnitudes in injury outcomes. The likelihood ratio tests indicate that injury severity must be analyzed and modeled separately for passenger cars, SUVs, pickup trucks, and minivans. Vehicle characteristics play a crucial role in driver distraction and crash outcomes. Analyzing a year of crash data, categorized by four vehicle types, has provided valuable insights into distracted driving patterns in passenger cars, SUVs, pickup trucks, and minivans, influencing potential prevention strategies. To combat against distracted driving effectively, priority should be given to driver education and training, roadway design, vehicle technology, enforcement, and automobile insurance. The automobile industry, especially for passenger cars, SUVs, pickup trucks, and minivans, should consider implementing advanced in-vehicle technologies tailored to the specific characteristics of each vehicle type (e.g., advanced driver assistance systems (ADAS)) to proactively prevent driver distraction. These proactive measures will contribute significantly to enhancing road safety and reducing the risks associated with distracted driving.

Unravelling the Complexity of Irregular Shiftwork, Fatigue and Sleep Health for Commercial Drivers and the Associated Implications for Roadway Safety.

Fatigue can be a significant problem for commercial motor vehicle (CMV) drivers. The lifestyle of a long-haul CMV driver may include long and irregular work hours, inconsistent sleep schedules, poor eating and exercise habits, and mental and physical stress, all contributors to fatigue. Shiftwork is associated with lacking, restricted, and poor-quality sleep and variations in circadian rhythms, all shown to negatively affect driving performance through impaired in judgment and coordination, longer reaction times, and cognitive impairment. Overweight and obesity may be as high as 90% in CMV drivers, and are associated with prevalent comorbidities, including obstructive sleep apnea, hypertension, and cardiovascular and metabolic disorders. As cognitive and motor processing declines with fatigue, driver performance decreases, and the risk of errors, near crashes, and crashes increases. Tools and assessments to determine and quantify the nature, severity, and impact of fatigue and sleep disorders across a variety of environments and populations have been developed and should be critically examined before being employed with CMV drivers. Strategies to mitigate fatigue in CMV operations include addressing the numerous personal, health, and work factors contributing to fatigue and sleepiness. Further research is needed across these areas to better understand implications for roadway safety.

An empirical analysis of driver injury severities in work-zone and non-work-zone crashes involving single-vehicle large trucks.

OBJECTIVES: Florida ranks among the states with the highest rates of work-zone crashes involving large trucks. With significant emphasis in Florida's strategic highway safety plan, understanding work-zone crashes involving large trucks and resulting injury severities is critically important. This study investigated the contributing factors influencing the driver injury severity of single-large-truck crashes in work zones, benchmarked against non-work zones in Florida. METHODS: Using work-zone and non-work-zone crash data from 2011 to 2019 (inclusive), driver-injury severities in single-large trucks crashes were studied using random parameters logit models that allow for possible heterogeneity in the means and variances of parameter estimates. The available data included a wide variety of factors known to influence driver injury severity, including spatial and temporal; vehicle and traffic; roadway, harmful events, and driver characteristics. RESULTS: The model estimates produced fundamental shift in unobserved heterogeneity for work-zone and non-work-zone crashes involving single large trucks. More importantly, the likelihood of large truck drivers' injury severity is about fourteen-times higher on rural and six-times higher on urban interstate highways and 1.3 times lower with 10 miles per hour below the posted speed limit for large trucks inside work zones relative to non-work zones. The model results also indicate that the likelihood of severe driver injury is higher for heavy truck (more than 26000 pounds), a lane-shift work-zone configuration, and careless driving in work-zone crashes involving single large trucks. CONCLUSIONS: The model findings add valuable insights to have profound effects in the safety performance of large trucks and in-vehicle safety technologies, such as, Advanced Driver Assistance Systems, for careful driving along the work-zone segments with lower speed, leading to Automated Driving Systems. These measures include various policy-related safety countermeasures including revisiting traffic control plan for lane-shift on highways specifically for large trucks and developing training modules for Florida registered truck drivers.

An analysis of single-vehicle truck crashes on rural curved segments accounting for unobserved heterogeneity.

INTRODUCTION: Medium to large truck crashes, particularly on rural curved roadways, lead to a disproportionately higher number of fatalities and serious injuries relative to other passenger vehicles over time. The intent of this study is to identify and quantify the factors affecting injury severity outcomes for single-vehicle truck crashes on rural curved segments in North Carolina. The crash data were extracted from the Highway Safety Information System (HSIS) from 2010 to 2017. METHOD: This study applied a mixed logit with heterogeneity in means and variances approach to model driver injury severity. The approach accounts for possible unobserved heterogeneity in the data resulting from driver, roadway, vehicle, traffic characteristics and/or environmental conditions. Results' Conclusion: The model results indicate that there is a complex interaction of driver characteristics such as demographics (male and female drivers, age below 30 years, and age between 50 to 65 years), driver physical condition (normal driving condition and sleepy while driving), driver actions (unsafe speed, overcorrection, and careless driving), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (undivided road, medium right shoulder width, graded surface, low and medium speed limit, low traffic volume), environmental conditions (rainy condition), vehicle characteristics (tractor-trailer and semi-trailer), and crashes characteristics (fixed object crashes and rollover crashes). In addition, this study compared the contributing factor leading to driver injury severity for curved and straight rural segments. Practical Applications: The results clearly indicate the importance of driving behavior, such as, exceeding the speed limit and careless driving along the high-speed curved segments, need to be prioritized for the trucking agency. Similarly, the suggested countermeasures for roadway design and maintenance agency encompass warning signs and advisory speed limit, roadside barrier with chevrons, and edge line rumble strips are important concerning curved segments in rural highways.

The effect of motorcyclists' age on injury severities in single-motorcycle crashes with unobserved heterogeneity.

INTRODUCTION: Due to the myriad of unique characteristics associated with motorcycle operation, motorcycle safety is a public health concern as complex as it is serious. National crash data suggest motorcyclists are 28 times more likely to be killed when compared to passenger car occupants. In the state of Florida, motorcycle crashes are 1.5 times more likely to result in the death of the rider, placing Florida among the top deadliest states for motorcyclists in the nation. Using police-reported data from 2016, this study addresses the complex and interconnected nature of the many characteristics associated with motorcycle operation by investigating the effect of age on motorcyclists' riding behavior as it relates to injury severity for single-motorcycle crashes in the state of Florida. METHOD: To account for unobserved heterogeneity in the crash data, mixed logit models with heterogeneity in means and variances were estimated to model three injury severity outcomes (non-visible, severe, and fatal) for three age groups (under 30, 30-49, and 50 and above). RESULTS: Model results indicate that age affects motorcyclists' safety perception and ability to assess risks, thereby influencing their involvement in risky behaviors. Characteristics unique to motorcycle operation-spatial characteristics, speed, motorcycle type, time of day, helmet usage, alcohol consumption, ejection from motorcycle, passenger presence, endorsement status, and lighting-are further complicated by their dependency on the characteristics of the individual motorcyclist. Age of motorcyclist indicates a relationship between motorcyclists' behavior and perceived safety. CONCLUSION: The model results indicated that statistically significant parameters constituted different models and they were not equal across the age groups of motorcyclists: aged under 30, aged 30-49, and aged 50 and above. Through advanced econometric modeling, this study fills a gap in the existing literature and assists the safety professionals, motorcycle trainers, policymakers, law enforcement agencies, and roadway designers in developing countermeasures.

The role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions.

The effect of inappropriate speed adjustment to adverse conditions on crash-injury severities, and how this effect might vary across male and female drivers, and over time, is not well understood. To study this, single-vehicle crashes occurring in rainy weather, where speed too fast for conditions is a driver action identified as a contributing factor to the crash, were considered. The differences between the resulting crash-injury severities of male and female drivers (and how these differences change over time) is then studied utilizing three years of Florida crash data and estimating random parameters multinomial logit models of driver injury severity while considering potential heterogeneity in the means and variances of parameter estimates. Model estimation results show that there were significant differences in the driver-injury severities of male and female drivers, and that the effect of factors that determine injury severities varied significantly over time (statistically significant temporal instability). This suggests that male and female drivers generally perceive and react to rainy weather conditions in fundamentally different ways, and that their responses, as reflected by the effect that explanatory variables have on injury severity probabilities, change over time. However, there were two explanatory variables that had relatively stable effects on injury-severity probabilities over time and across genders: an indicator variable for crashes involving non-collision factors (including overturn/rollover crashes) and an indicator variable for restraint usage. Policies that target these two variables could produce long-term reductions in crash-injury severities under adverse conditions.