Prevalence of alcohol impairment and odds of a driver injury or fatality in on-road farm equipment crashes

Objective: The objective of this article was to estimate the prevalence of alcohol impairment in crashes involving farm equipment on public roadways and the effect of alcohol impairment on the odds of crash injury or fatality.

Methods: On-road farm equipment crashes were collected from 4 Great Plains state departments of transportation during 2005–2010. Alcohol impairment was defined as an involved driver having blood alcohol content of ≥0.08 g/100 ml or a finding of alcohol impairment as a driver contributing circumstance recorded on the police crash report. Injury or fatality was categorized as (a) no injury (no and possible injury combined), (b) injury (nonincapacitating or incapacitating injury), and (c) fatality. Hierarchical multivariable logistic regression modeling, clustered on crash, was used to estimate the odds of an injury/fatality in crashes involving an alcohol-impaired driver.

Results: During the 5 years under study, 3.1% (61 of 1971) of on-road farm equipment crashes involved an alcohol-impaired driver. One in 20 (5.6%) injury crashes and 1 in 6 (17.8%) fatality crashes involved an alcohol-impaired driver. The non-farm equipment driver was significantly more likely to be alcohol impaired than the farm equipment driver (2.4% versus 1.1% respectively, P = .0012). After controlling for covariates, crashes involving an alcohol-impaired driver had 4.10 (95% confidence interval [CI], 2.30–7.28) times the odds of an injury or fatality. In addition, the non-farm vehicle driver was at 2.28 (95% CI, 1.92–2.71) times higher odds of an injury or fatality than the farm vehicle driver. No differences in rurality of the crash site were found in the multivariable model.

Conclusion: On-road farm equipment crashes involving alcohol result in greater odds of an injury or fatality. The risk of injury or fatality is higher among the non-farm equipment vehicle drivers who are also more likely to be alcohol impaired. Further studies are needed to measure the impact of alcohol impairment in on-road farm equipment crashes.     

Driver's alcohol and passenger's death in motor vehicle crashes

OBJECTIVE: Previous studies on alcohol involvement associated with fatal injury in traffic crashes have focused on the drivers, but the passenger's view is not well known. This study (1) analyzes the relationship between passenger's death and alcohol inebriation of the driver and (2) estimates the role of alcohol as the cause of a crash by examining who was at fault, sober, or inebriated. METHOD: The study includes all motor vehicle passengers (n = 420) who died in crashes in Sweden 1993 through 1996 and were medicolegally autopsied. Autopsy reports from the Departments of Forensic Medicine, including toxicological analyses, and police reports were studied. Presence of alcohol among drivers was based on blood and breath tests. RESULTS: One-fifth of the fatally injured passengers and one-fifth of the tested drivers were under the influence of alcohol. The youngest drivers had the highest prevalence of drunken driving. Drivers at fault were alcohol positive in 21% of these crashes and drivers were not at fault in 2% of these crashes. In 53% of the crashes where both the passenger and driver were alcohol positive, the passenger had a lower alcohol concentration than the driver. Children (<16 years) comprised 15% of the killed passengers. Notably, the children were riding with a driver who was under influence of alcohol in 13% of these crashes. Alcohol involvement was not tested in half of the surviving drivers. CONCLUSIONS: The data show that 20% of both passengers and drivers were under the influence of alcohol. Increased testing of surviving drivers regarding alcohol and other drugs is recommended.     

Injury Outcome in Crashes with Guardrail End Terminals

Objective: The goal of this study is to evaluate the crash performance of guardrail end terminals in real-world crashes. Guardrail end terminals are installed at the ends of guardrail systems to prevent the rail from spearing through the car in an end-on collision. Recently, there has been a great deal of controversy as to the safety of certain widely used end terminal designs, partly because there is surprisingly little real-world crash data for end terminals. Most existing studies of end terminal crashes used data from prior to the mid-1990s. Since then, there have been large improvements to vehicle crashworthiness and seat belt usage rates, as well as new roadside safety hardware compliant with National Cooperative Highway Research Program (NCHRP) Report 350, “Recommended Procedures for the Safety Performance Evaluation of Highway Features.” Additionally, most existing studies of injury in end terminal crashes do not account for factors such as the occurrence of rollover. This analysis uses more recent crash data that represent post-1990s vehicle fleet changes and account for a number of factors that may affect driver injury outcome and rollover occurrence.

Methods: Passenger vehicle crashes coded as involving guardrail end terminals were identified in the set of police-reported crashes in Michigan in 2011 and 2012. End terminal performance was expected to be a function of end terminal system design. State crash databases generally do not identify specific end terminal systems. In this study, the coded crash location was used to obtain photographs of the crash site prior to the crash from Google Street View. These site photographs were manually inspected to identify the particular end terminal system involved in the crash. Multiple logistic regression was used to test for significant differences in the odds of driver injury and rollover between different terminal types while accounting for other factors.

Results: A total of 1,001 end terminal crashes from the 2011–2012 Michigan State crash data were manually inspected to identify the terminal that had been struck. Four hundred fifty-one crashes were found to be suitable for analysis. Serious to fatal driver injury occurred in 3.8% of end terminal crashes, moderate to fatal driver injury occurred in 11.8%, and 72.3% involved property damage only. No significant difference in moderate to fatal driver injury odds was observed between NCHRP 350 compliant end terminals and noncompliant terminals. Car drivers showed odds of moderate to fatal injury 3.6 times greater than LTV drivers in end terminal crashes. Rollover occurrence was not significantly associated with end terminal type.

Conclusions: Car drivers have greater potential for injury in end terminal crashes than light truck/van/sport utility vehicle drivers. End terminal designs compliant with NCHRP 350 did not appear to carry different odds of moderate driver injury than noncompliant end terminals. The findings account for driver seat belt use, rollover occurrence, terminal orientation (leading/trailing), control loss, and the number of impact events. Rollover and nonuse of seat belts carried much larger increases in injury potential than end terminal type. Rollover did not appear to be associated with NCHRP 350 compliance.     

Analyses of rear-end crashes based on classification tree models

OBJECTIVE: Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. METHODS: Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. RESULTS: The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. CONCLUSIONS: Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.     

Issues with using police citations to assign responsibility in quasi-induced exposure

Crash fault determination is one of the most critical issues in applications of quasi-induced exposure. Traditionally, the driver citation issued by the investigating police officer is the primary source to assign responsibility for motor vehicle crashes. Such citations are based on the “evidence” or observation of a moving violation (such as engaged hazardous actions) in combination with non-moving violations (such as suspended driver license) prior to the crash. The objective here is to identify the contributing factors that may lead to driver citations in two-vehicle crashes in addition to the hazardous action. Multivariate binary logistic regression modeling is employed to explore the behavior of the investigating police officer in terms of issuing citation at the crash scene. A series of explanatory parameters including roadway characteristics, environmental factors, and driver and vehicle attributes is assessed. The results show that whether the crash type was a hit-and-run, alcohol and illegal drug use, driver gender, driver age, and injury severity all appear to have significant impacts on the investigating officer’s decision-making. Specific examples are given to demonstrate how two factors hit-and-run and drinking status can skew the exposure estimates in the context of quasi-induced exposure. The findings will help to serve as a basis to select appropriate parameters in assigning crash responsibility in quasi-induced exposure applications; and we make recommendations to modify existing crash database for better safety research in the future.     

The detection of drowsiness using a driver monitoring system

Abstract

Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors.

Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4?h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slower- and faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources.

Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897.

Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.     

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

Objective: The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

Methods: Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

Results: Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

Conclusions: This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50–60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol.

When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day.

The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.     

The use of technology to address patterns of risk among teenage drivers

INTRODUCTION: The crash risk of teens is high, with fatal crash rates of teen drivers higher than any other age group. New approaches to reduce teen traffic fatalities are clearly needed. METHOD: A possible approach to reduce the incidence of teen driver crashes and fatalities is through the use of vehicle-based intelligent driver support systems. To be most effective, the system should address the behaviors associated with an overwhelming number of teen fatal crashes: speed, low seatbelt use, and alcohol impairment. In-vehicle technology also offers an opportunity to address the issue of inexperience through enforcement of certain Graduated Driver's License provisions. RESULTS: To fully understand the capability of such technologies, there should be a concerted effort to further their development, and human factors testing should take place to understand their effects on the driver. IMPACT: If successfully implemented, a Teen Driver Support System (TDSS), such as the one described here, could significantly decrease the number of teens killed in traffic crashes.     

Teenage driver crash incidence and factors influencing crash injury by rurality

BackgroundPrevious research has identified teenage drivers as having an increased risk for motor-vehicle crash injury compared with older drivers, and rural roads as having increased crash severity compared with urban roads. Few studies have examined incidence and characteristics of teen driver-involved crashes on rural and urban roads.MethodsAll crashes involving a driver aged 10 through 18 were identified from the Iowa Department of Transportation crash data from 2002 through 2008. Rates of overall crashes and fatal or severe injury crashes were calculated for urban, suburban, rural, and remote rural areas. The distribution of driver and crash characteristics were compared between rural and urban crashes. Logistic regression was used to identify driver and crash characteristics associated with increased odds of fatal or severe injury among urban and rural crashes.ResultsFor younger teen drivers (age 10 through 15), overall crash rates were higher for more rural areas, although for older teen drivers (age 16 through 18) the overall crash rates were lower for rural areas. Rural teen crashes were nearly five times more likely to lead to a fatal or severe injury crash than urban teen crashes. Rural crashes were more likely to involve single vehicles, be late at night, involve a failure to yield the right-of-way and crossing the center divider.ConclusionsIntervention programs to increase safe teen driving in rural areas need to address specific risk factors associated with rural roadways.Impact on IndustryTeen crashes cause lost work time for teen workers as well as their parents. Industries such as safety, health care, and insurance have a vested interest in enhanced vehicle safety, and these efforts should address risks and injury differentials in urban and rural roadways.     

Temporal factors in motor-vehicle crash deaths: Ten years later

ObjectiveTo assess trends in traffic fatalities on several temporal scales: year to year, by month, by day of week, and by time of day, to determine why some times correspond with higher rates of crash deaths, and to assess how these trends relate to age, the role of the deceased, and alcohol consumption.MethodTraffic fatalities were identified using the Fatality Analysis Reporting System (FARS) for 1998 through 2014 and assessed for their time of occurrence. Three days that, on average, contained particularly high numbers of crash deaths were then assessed in greater detail, considering the age of the deceased, role of the deceased (vehicle occupant, bicyclist, motorcyclist, or pedestrian), and the blood alcohol content of either the driver (for passenger vehicle occupants) or the deceased.ResultsAnnual crash fatality totals were much lower in 2014 than in 1998, but the decrease was not steady; a marked drop in crash deaths occurred after 2007 and continued until 2014. On average the most fatalities per day occurred in July and August (116 per day), followed closely by June, September, and October. During the week, the greatest number of fatalities on average occur on weekend days, and during the day the most fatalities tend to occur between the hours of 3 p.m. and 7 p.m. Holidays like Independence Day and New Year's Day show elevated crash fatalities, and a greater percentage of these crashes involved alcohol, when compared with adjacent days.ConclusionCertain days and times of year stand out as posing an elevated crash risk, and even with the decrease in average daily fatalities over the past decade, these days and times of year have remained consistent.Practical applicationThese results indicate focused areas for continued efforts to reduce fatal crashes.     

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

Fatal hit-and-run crashes: Factors associated with leaving the scene

Introduction: Hit-and-run crashes are a criminal offense that leave the victim without prompt medical care or the ability to receive financial compensation. Method: The purpose of the current study was to quantify the factors associated with the probability that a driver leaves the scene of a fatal crash, using multiple imputation to incorporate information from drivers who were never apprehended and thus whose characteristics were unknown. Results: The results of this study show that in addition to driver, vehicle, and environmental factors having significant impacts on the likelihood of a driver fleeing the scene, economic and demographic factors are important as well. Practical Applications: This analysis allows for a more holistic understanding of hit-and-run crashes and informs potential countermeasures and future research.     

Using event-triggered naturalistic data to examine the prevalence of teen driver distractions in rear-end crashes

IntroductionWhile teen driver distraction is cited as a leading cause of crashes, especially rear-end crashes, little information is available regarding its true prevalence. The majority of distraction studies rely on data derived from police reports, which provide limited information regarding driver distraction.MethodThis study examined over 400 teen driver rear-end crashes captured by in-vehicle event recorders. A secondary data analysis was conducted, paying specific attention to driver behaviors, eyes-off-road time, and response times to lead-vehicle braking.ResultsAmong teens in moderate to severe rear-end crashes, over 75% of drivers were observed engaging in a potentially distracting behavior. The most frequently seen driver behaviors were cell phone use, attending to a location outside the vehicle, and attending to passengers. Drivers using a cell phone had a significantly longer response time than drivers not engaged in any behaviors, while those attending to passengers did not. Additionally, in about 50% of the rear-end crashes where the driver was operating/looking at a phone (e.g., texting), the driver showed no driver response (i.e., braking or steering input) before impact, compared to 10% of crashes where the driver was attending to a passenger.ConclusionsThe high frequency of attending to passengers and use of a cell phone leading up to a crash, compounded with the associated risks, underlines the importance of continued investigation in these areas.Practical applicationsParents and teens must be educated regarding the frequency of and the potential effects of distractions. Additional enforcement may be necessary if Graduated Driver Licensing (GDL) programs are to be effective. Systems that alert distracted teens could also be especially helpful in reducing rear-end collisions.     

Traffic environment and demographic factors affecting impaired driving and crashes

IntroductionData availability has forced researchers to examine separately the role of alcohol among drivers who crashed and drivers who did not crash. Such a separation fails to account fully for the transition from impaired driving to an alcohol-related crash.MethodIn this study, we analyzed recent data to investigate how traffic-related environments, conditions, and drivers’ demographics shape the likelihood of a driver being either involved in a crash (alcohol impaired or not) or not involved in a crash (alcohol impaired or not). Our data, from a recent case–control study, included a comprehensive sampling of the drivers in nonfatal crashes and a matched set of comparison drivers in two U.S. locations. Multinomial logistic regression was applied to investigate the likelihood that a driver would crash or would not crash, either with a blood alcohol concentration (BAC) = .00 or with a BAC ≥ .05.ConclusionsTo our knowledge, this study is the first to examine how different driver characteristics and environmental factors simultaneously contribute to alcohol use by crash-involved and non-crash-involved drivers. This effort calls attention to the need for research on the simultaneous roles played by all the factors that may contribute to motor vehicle crashes.     

Use of empirical and full Bayes before–after approaches to estimate the safety effects of roadside barriers with different crash conditions

IntroductionAlthough many researchers have estimated the crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of prior studies that have explored the variation of CMFs. Thus, the main objectives of this study are: (a) to estimate CMFs for the installation of different types of roadside barriers, and (b) to determine the changes of safety effects for different crash types, severities, and conditions.MethodTwo observational before–after analyses (i.e. empirical Bayes (EB) and full Bayes (FB) approaches) were utilized in this study to estimate CMFs. To consider the variation of safety effects based on different vehicle, driver, weather, and time of day information, the crashes were categorized based on vehicle size (passenger and heavy), driver age (young, middle, and old), weather condition (normal and rain), and time difference (day time and night time).ResultsThe results show that the addition of roadside barriers is safety effective in reducing severe crashes for all types and run-off roadway (ROR) crashes. On the other hand, it was found that roadside barriers tend to increase all types of crashes for all severities. The results indicate that the treatment might increase the total number of crashes but it might be helpful in reducing injury and severe crashes. In this study, the variation of CMFs was determined for ROR crashes based on the different vehicle, driver, weather, and time information.Practical applicationsBased on the findings from this study, the variation of CMFs can enhance the reliability of CMFs for different roadway conditions in decision making process. Also, it can be recommended to identify the safety effects of specific treatments for different crash types and severity levels with consideration of the different vehicle, driver, weather, and time of day information.     

The effect of a campus-community environmental alcohol prevention initiative on student drinking and driving: results from the "a matter of degree" program evaluation

OBJECTIVE: Motor vehicle crashes involving alcohol are a major contributor to morbidity and mortality among college students in the United States. This study evaluates the effect on drinking and driving outcomes of the "A Matter of Degree" program, a campus-community coalition initiative to reduce college binge drinking. METHODS: We used a quasi-experimental longitudinal study design that compared student responses at 10 colleges participating in the program and students attending 32 similar colleges that did not participate in the program. We also divided the program sites into two groups of five according to their level of program implementation and compared each with the non-program colleges. We examined driving after any alcohol consumption and driving after five or more drinks among drinkers who drove one or more times a week per month and riding with a high or drunk driver among all students at these colleges beginning in 1997 through 2001. Outcomes were based on data collected from repeated cross-sectional surveys using the Harvard School of Public Health College Alcohol Study. Analyses were conducted using MLwiN multilevel statistical software. RESULTS: We found significant reductions in driving after drinking, driving after five or more drinks and riding with a high or drunk driver at the program colleges relative to the comparison colleges. Further analyses indicated that these reductions among the AMOD program colleges occurred at the sites with high program implementation relative to comparison sites, while no statistically significant change was noted at the program sites with low implementation. The program effect on the two drinking and driving outcomes appeared to be mediated by frequent binge drinking, while significant decline in the riding with an intoxicated driver outcome was not mediated by the individual's drinking. CONCLUSIONS: Campus-community based environmental alcohol prevention is a promising approach for reducing alcohol-impaired motor vehicle crashes among this population.     

Validity of police-reported alcohol involvement in fatal motor carrier crashes in the United States between 1982 and 2005

Objective

To examine the validity of police-reported alcohol data for drivers involved in fatal motor carrier crashes.

Material and Methods

We determined the availability of blood alcohol concentration (BAC) and police-reported alcohol data on 157,702 drivers involved in fatal motor carrier crashes between 1982 - 2005 using Fatality Analysis and Reporting System (FARS) data. Drivers were categorized as motor carrier drivers if they operated a vehicle with a gross vehicle weight rating of greater than 26,000 pounds. Otherwise, they were classified as non motor carrier drivers. The sensitivity and specificity of police-reported alcohol involvement were estimated for both driver types.

Results

Of the 157,702 drivers, 18% had no alcohol information, 15% had BAC results, 42% had police-reported alcohol data, and 25% had both. Alcohol information varied significantly by driver, crash, and vehicle characteristics. For example, motor carrier drivers were significantly more likely (51%) to have BAC testing results compared to non motor carrier drivers (31%) (p < 0.001). The sensitivity of police-reported alcohol involvement for a BAC level ≥ 0.08 was 83% (95% CI 79%, 86%) for motor carrier drivers and 90% (95% CI 89%, 90%) for non motor carrier drivers. The specificity rates were 96% (95% CI 95%, 96%) and 91% (95% CI 90%, 91%), respectively.

Conclusions

The sensitivity and specificity of police-reported alcohol involvement are reasonably high for drivers involved in fatal motor carrier crashes. Further research is needed to determine the extent to which the accuracy of police-reported alcohol involvement may be overestimated because of verification bias.

Impact on the Industry

Based on the results of this study, the federal government should continue to work with states to strengthen their strategies to increase chemical testing of all drivers involved in fatal crashes.     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

Temporal patterns of crashes of 16-to 17-year-old drivers in Fairfax County, Virginia

OBJECTIVE: To develop a better understanding of the frequency and characteristics of teenage driver crashes occurring during school commute times. METHOD: Data were obtained from police reports of crashes involving drivers ages 16-17 that occurred between September 2001 and August 2004 in Fairfax County, Virginia. Temporal patterns and other characteristics of crash involvement during the school year were examined, and crashes during school commute times were compared with those at other times. RESULTS: Teenage driver crash involvement spiked during weekday school commute times. Compared with other times, crashes during school commute times were significantly more likely to involve multiple vehicles but less likely to result in injuries or involve drivers who were male, made driving errors, or had been drinking alcohol. Crashes during school commute times were more likely to involve more than one teenage driver and occur close to schools. CONCLUSIONS: Crashes involving teenage drivers are prevalent during school commute times. Many of these crashes involve multiple teenage drivers and occur near schools. Schools and communities should consider programs and policies that reduce teenage driving to school and enhance the safety of teenagers that do drive.     

Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming

Introduction: The state of Wyoming, like other western United States, is characterized by mountainous terrain. Such terrain is well noted for its severe downgrades and difficult geometry. Given the specific challenges of driving in such difficult terrain, crashes with severe injuries are bound to occur. The literature is replete with research about factors that influence crash injury severity under different conditions. Differences in geometric characteristics of downgrades and mechanics of vehicle operations on such sections mean different factors may be at play in impacting crash severity in contrast to straight, level roadway sections. However, the impact of downgrades on injury severity has not been fully explored in the literature. This study is thus an attempt to fill this research gap. In this paper, an investigation was carried out to determine the influencing factors of crash injury severities of downgrade crashes. Method: Due to the ordered nature of the response variable, the ordered logit model was chosen to investigate the influencing factors of crash injury severities of downgrade crashes. The model was calibrated separately for single and multiple-vehicle crashes to ensure the different factors influencing both types of crashes were captured. Results: The parameter estimates were as expected and mostly had signs consistent with engineering intuition. The results of the ordered model for single-vehicle crashes indicated that alcohol, gender, road condition, vehicle type, point of impact, vehicle maneuver, safety equipment use, driver action, and annual average daily traffic (AADT) per lane all impacted the injury severity of downgrade crashes. Safety equipment use, lighting conditions, posted speed limit, and lane width were also found to be significant factors influencing multiple-vehicle downgrade crashes. Injury severity probability plots were included as part of the study to provide a pictorial representation of how some of the variables change in response to each level of crash injury severity. Conclusion: Overall, this study provides insights into contributory factors of downgrade crashes. The literature review indicated that there are substantial differences between single- and multiple vehicle crashes. This was confirmed by the analysis which showed that mostly, separate factors impacted the crash injury severity of the two crash types. Practical applications: The results of this study could be used by policy makers, in other locations, to reduce downgrade crashes in mountainous areas.