Fatality Analysis Reporting System Research Articles

Characteristics of vehicles driven by teens and adults killed in crashes, 2013–2017

Introduction: Teen drivers experience higher crash risk than their experienced adult counterparts. Legislative and community outreach methods have attempted to reduce this risk; results have been mixed. The increasing presence of vehicle safety features across the fleet has driven fatality numbers down in the past decades, but the disparity between young drivers and others remains. Method: We merged Fatality Analysis Reporting System (FARS) data on fatal crashes with vehicle characteristic data from the Highway Loss Data Institute (HLDI). The analysis compared the vehicle type, size, age, and the presence of select safety features in vehicles driven by teens (ages 15–17 years) and adult drivers (ages 35–50 years) who were killed in crashes from 2013 to 2017. Results were compared with a similar analysis conducted on data from 2007 to 2012. Results: Teen drivers were more likely than their adult counterparts to be killed while driving older, smaller vehicles that were less likely to have the option to be equipped with side airbags. Discussion: Teenage drivers remain more likely to be killed while driving older, smaller vehicles than adult drivers. Parents and guardians are mainly responsible for teen vehicle choice, and should keep vehicle size, weight, and safety features in mind when placing their teen in a vehicle. Practical Application: These findings can help guide safer vehicle choice for new teen drivers.

Toward Reducing the Possibility of False-Positive Results in Epidemiologic Studies of Traffic Crashes

Large databases, such as the U.S. Fatality Analysis Reporting System (FARS), are often used to study factors that influence traffic crashes that result in injuries or deaths, and how they may be af...

Drug presence in driving deaths in Maryland: Comparing trends and prevalence in medical examiner and FARS data

Accurate and reliable information on drug use by road users is essential to inform safety policy development but the reliability of national data has been questioned. There are two primary repositories of drug test information from fatal motor vehicle crashes in Maryland: (1) the Fatality Analysis Reporting System (FARS), which is a national crash database managed by the US Department of Transportation, and (2) the Maryland Medical Examiner (ME). In this study, we compared drug test information for people killed in crashes in Maryland between the FARS system and ME from 2006 – 2018. As ME records are the primary source for the FARS data from Maryland, these two data sets should be closely correlated. We used probabilistic linkage to match FARS and ME cases and compared matched cases by individual drug group. Matching was achieved on 83 % of cases (N = 4803 matched pairs). ME data consistently indicated higher overall incidence and trends in the presence of depressants, narcotics, and stimulants in crash deaths. Sensitivity analysis using both strict and relaxed matching criteria did not change this result. Road safety policy and prevention efforts for crashes involving drugs and alcohol require an accurate understanding of both long-term trends and year-to-year changes in drug prevalence. These findings demonstrate the potential value of using ME data as source of drug test information for crash deaths in the United States.

A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City’s Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City’s Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size.

Association between access to pediatric trauma care and motor vehicle crash death in children: An ecologic analysis of United States counties.

Access to pediatric trauma care is highly variable across the United States. The purpose of this study was to measure the association between pediatric trauma center care and motor vehicle crash (MVC) mortality in children (<15 years) at the US county level for 5 years (2014-2018). The exposure was defined as the highest level of pediatric trauma care present within each county: (1) pediatric trauma center, (2) adult level 1/2, (3) adult level 3, or (4) no trauma center. Pediatric deaths due to passenger vehicle crashes on public roads were identified from the NHTSA Fatality Analysis Reporting System. Hierarchical negative binomial modeling measured the relationship between highest level of pediatric trauma care and pediatric MVC mortality within counties. Adjusted analyses accounted for population age and sex, emergency medical service response times, helicopter ambulance availability, state traffic safety laws, and measures of rurality. During the study period 3,067 children died in fatal crashes. We identified 188 pediatric trauma centers in 141 counties. Significant disparities in access to pediatric trauma care were observed. Specifically, 99% of pediatric trauma centers were situated in population-dense urban counties, while 28% of children lived in counties no trauma center. After risk adjustment, counties with pediatric trauma centers had significantly lower rates of pediatric MVC death than those with no trauma center: 0.7 versus 3.2 deaths/100,000 child-years; mortality rate ratio, 0.58; and 95% confidence interval, 0.39 to 0.86. In counties where pediatric trauma centers were absent, adult level 1/2 trauma centers were associated with comparable risk reduction. The presence of pediatric trauma centers was associated with lower rates of MVC death in children. Adult level 1/2 trauma centers appear to offer comparable risk reduction. Where population differences in pediatric trauma mortality are observed, addressing disparities in county-level access to pediatric trauma care may serve as a viable target for system-level improvement. Epidemiological, level III; Care management, level III.

Frequency and cost of crashes, fatalities, and injuries involving disabled vehicles

ObjectiveTo quantify the total number and cost of crashes, fatalities, and injuries that could be addressed by improved conspicuity of disabled vehicles to approaching traffic. MethodsUsing the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS) this study defines three crash scenarios where insufficient conspicuity of a disabled vehicle (“low conspicuity emergency”) resulted in injury or death: Scenario 1) Moving vehicle strikes non-moving vehicle following an initial event; Scenario 2) Pedestrian (primarily a motorist who has exited their vehicle) is struck while tending to a disabled or stopped vehicle; and Scenario 3) A vehicle departs the roadway and crashes unnoticed and rescue initiation is delayed significantly. ResultsAnnually, between the years 2016 and 2018, an estimated 71,693 people were involved in low conspicuity emergency events, including 566 fatalities and 14,371 injuries. Most (95 %) of these cases occurred under scenario 1. Notable, however, is the severity of scenario 2 crashes where the majority were severely injured (22 %) or killed (19 %). Based on the FARS data, nearly 300 people were killed under scenario 2 each year and cases have increased 27 % since 2014. Overall, crashes under these three scenarios resulted in an annual estimated $8.8 billion in societal costs, including the economic costs of medical payments and wage loses in addition to the value of quality of life lost due to death or disability. Scenario 1 crashes resulted in an average of $4.3 billion in losses, scenario 2 crashes in $3.4 billion in losses, and scenario 3 crashes in $1.2 billion in losses annually. ConclusionsA significant number of people die or are injured in low conspicuity events every year; an estimated 1.55 deaths and nearly 40 injuries per day. This analysis highlights the risks to a special subset of pedestrians: motorists who exited their vehicles to attend to a disabled or stopped vehicle. These deaths and injuries that result from crashes related to low-conspicuity events are preventable. Countermeasures to reduce the incidence and severity of the crash scenarios studied should be explored.

The crash injury severity prediction of traffic accident using an improved wrappers feature selection algorithm

The crash injury severity prediction of traffic accident is the prerequisite for driving decisions under dangerous conditions. This paper puts forward a new wrapper feature selection algorithm to improve the prediction accuracy of the injury severity. Firstly, based on Gini index and Mutual information, the importance of factors is analyzed by the comprehensive evaluation criteria, according to the importance score, the factors were selected by sequential backward selection. Secondly, three models, that is RF (Random Forest), C4.5 (C4.5 Decision Tree) and SVM (Support Vector Machine) are established to verify the prediction accuracy of selected subsets. In order to verify the proposed algorithm, based on the FARS(Fatality Analysis Reporting System) in the NHTSA(National Highway Traffic Safety Administration), a total of 6,295 rear-end accidents including 23 candidate factors were screened, the results demonstrate that the accuracy of the three models increases with the elimination of redundant variables, among them the SVM model has better predictive performance, the significant factors are obtained which include seat belts, airbag, the relative speed, vehicle weight, etc. finally the sensitivity analysis of significant factors is implemented and some suggestions are given to improve traffic safety.

Association between legalization of recreational cannabis and fatal motor vehicle collisions in the United States: an ecologic study.

Background:With the recent legalization of recreational cannabis in Canada, cannabis-impaired driving is an important public safety concern. Our aim was to examine the association between recreational cannabis legalization and fatal motor vehicle collisions using data from the United States, which present a timely natural experiment of cannabis legalization.Methods:We conducted an ecologic study using the number of fatal motor vehicle collisions and the associated number of deaths for US jurisdictions with legalized recreational cannabis (2007–2018) retrieved from the US Fatality Analysis Reporting System. We examined jurisdiction-specific rates of fatal motor vehicle collisions and associated deaths before and after recreational cannabis legalization using Poisson regression and meta-analyzed estimates across jurisdictions using DerSimonian and Laird random-effects models.Results:After adjustment for calendar year, legalization was associated with increases in rates of fatal motor vehicle collisions (incidence rate ratio [IRR] 1.15, 95% confidence interval [CI] 1.06–1.26) and associated deaths (IRR 1.16, 95% CI 1.06–1.27). Differences between the first 12 months after legalization relative to subsequent months were inconclusive for rates of fatal motor vehicle collisions (IRR 0.92, 95% CI 0.84–1.02) and associated deaths (IRR 0.92, 95% CI 0.84–1.01).Interpretation:Recreational cannabis legalization in the US was associated with a relative increased risk of fatal motor vehicle collisions of 15% and a relative increase in associated deaths of 16%, with no conclusive difference between the first and subsequent years after legalization. These findings raise concern that there could be a similar increase in fatal motor vehicle collisions and associated deaths in Canada following recreational cannabis legalization.

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.

Crash report data analysis for creating scenario-wise, spatio-temporal attention guidance to support computer vision-based perception of fatal crash risks

Reducing traffic fatal crashes has been an important mission of transportation. With the rapid development of sensor and Artificial Intelligence (AI) technologies, the computer vision (CV)-based crash anticipation in the near-crash phase is receiving growing attention. The ability to perceive fatal crash risks in an early stage is of paramount importance as well because it can improve the reliability of crash anticipation. Yet this task is challenging because it requires establishing a relationship between the driving scene information that CV can recognize and the fatal crash features that CV will not get until the crash occurrence. Image data with the annotation for directly training a reliable AI model for the early visual perception of fatal crash risks are not abundant. The Fatality Analysis Reporting System (FARS) contains big data on fatal crashes, which is a reliable data source for finding fatal crash clusters and discovering their distribution patterns to tell the association between driving scene characteristics and fatal crash features. To enhance CV's ability to perceive fatal crash risks earlier, this paper develops a data analytics model from fatal crash report data, which is named scenario-wise, spatio-temporal attention guidance. First, the paper identifies five descriptive variables that are sparse and thus allow for decomposing the 5-year (2013–2017) fatal crash dataset to develop scenario-wise attention guidance. Then, an exploratory analysis of location- and time-related descriptive variables suggests dividing fatal crashes into spatially defined groups. A group's temporal distribution pattern is an indicator of the similarity of fatal crashes in the group. Hierarchical clustering and K-means clustering further merge the spatially defined groups into six clusters according to the similarity of their temporal patterns. After that, association rule mining discovers the statistical relationship between the temporal information of driving scenes with fatal crash features, such as the first harmful event and the manner of collisions, for each cluster. The paper illustrates how the developed attention guidance supports the design and implementation of a preliminary CV model that can identify agents of a possibility to involve in fatal crashes from their environmental and context information.

A penalized-likelihood approach to characterizing bridge-related crashes in New Jersey

Objective A roadway departure crash is one in which a vehicle crosses an edge line, a centerline, or otherwise leaves the traveled way. These crashes that involve run-off-road and cross-median/centerline head-on collisions tend to be more severe than other crash types. According to the NHTSA Fatality Analysis Reporting System database, a total of 7,833 people perished in crashes involving fixed roadside objects in 2017, accounting for 21 percent of the total number of fatalities in the United States. Several previous studies have reported that rural bridge-related crashes result in more fatalities due to their being mostly the fixed-object crash type. As such, further in-depth investigation of this type of crash is necessary. Due to the lack of a comprehensive database that includes bridge-related crashes and bridge characteristics, identifying the key factors contributing to this type of crash is a challenging task that is addressed in this paper. Method Study team gathered and compiled five years (2011–2015) of crash data from the New Jersey crash database and the characteristics of bridges from the Long-Term Bridge Performance portal. A Firth’s penalized-likelihood logistic regression model was developed to examine the impact of explanatory variables on crash severity. Results Based on the five years (2011–2015) of crash data, significant factors (i.e., driver age, weather conditions, surface conditions, lighting conditions, speed limit, roadway characteristics, and direction of traffic) were identified that affect the severity of bridge-related crashes in Middlesex County, New Jersey. Conclusion This model is an appropriate tool for predicting the impact of all the confounding variables on the probability of bridge-related crashes while also considering the rareness of the event. Based on the obtained odds ratio, the various effects of the identified variables are discussed, and recommendations made regarding countermeasures policymakers can establish to reduce the number of these crashes in New Jersey.

Alcohol- and speeding-related fatal crashes among novice drivers age 18–20 not fully licensed at the time of the crash

IntroductionSome teens may intentionally delay their driving licensure until age 18 or later, thereby skipping the learning and driver safety benefits provided by graduated driver licensing (GDL) programs. Delaying driving initiation could elevate teen crash risk once they begin driving. This study compares the prevalence of alcohol use and speeding among individuals aged 18–20 y/o who were not fully licensed drivers (NFLD) at the time of the fatal crash with that of their fully licensed counterparts (FLD). Materials and methodsData came from the 2010–2017 Fatality Analysis Reporting System (FARS). Census data were added to measure mean household income at drivers’ zip code. Bivariate (chi-square) and logistic regressions were used to assess the likelihood young drivers were alcohol-positive and/or speeding at the time of the crash as a function of drivers’ license status, demographic, socioeconomic, and the strength of GDL programs in the state. ResultsCompared with females and White drivers age 18–20 y/o, males, Latino, and Black drivers were more likely to be NFLD at the time of the fatal crash (p < .0001). Living in zip codes with low median household income (p < .001) and the strength of GDL restrictions (p < .0001) were associated with individuals being NFLD at the time of the fatal crash. Alcohol-related fatal crashes were more prevalent among NFLD than FLD (p < .02). ConclusionsThe study provides indirect evidence suggesting that alcohol use may be more detrimental for NFLD than for FLD.

Investigation of the Relationship between Rainfall and Fatal Crashes in Texas, 1994–2018

Understanding how crash factors are impacted by rain is critical to road safety planning and management. This study assesses the impact of rain on traffic safety by conducting an analysis of the fatal crashes related to rain in Texas from 1994 to 2018. The fatal crash data was gathered from the Fatality Analysis Reporting System (FARS) database maintained by the National Highway Traffic Safety Administration (NHTSA). Environmental variables used in the analysis include month of the year, time of the day, temperature, and weather condition. The roadway-related factors identified include the posted speed limit, the number of lanes, route sign, and Vehicle Miles Traveled (VMT). The driver-related factors identified include age, gender, and the number of licensed drivers in total. Relative risk analysis was performed to statistically quantify the impact of rainy conditions at the hourly and monthly time scales. On average, rain-related fatal crashes represented about 6.8% of the total fatal crashes. However, the proportion shows higher variability at the annual, monthly, and hourly time scales and seems to be influenced by other factors such as the age and gender of the driver, type of the road, and posted roadway speed limit. Total and rain-related crashes show statistically significant decreasing trends when normalized by the total number of licensed drivers or vehicle miles travelled. The relative risk of a fatal crash during rainy conditions was always greater than 1.0 at hourly, monthly, and annual time scales. However, it shows significant variability at the monthly (1.07 to 2.78) and hourly scales (1.35 to 2.57). The relative risk is higher in less urbanized and drier counties, in general. Gender and age analysis reveals that male and young drivers are more likely to be involved in a fatal crash but less likely to be killed in the crash.

Is bicycling getting safer? Bicycle fatality rates (1985–2017) using four exposure metrics

The National Highway Traffic Safety Administration cites decreasing bicyclist fatalities in the United States over the past several decades as evidence of improved bicycling safety. However, with users possessing a diverse range of tolerances, purposes, and socio-demographics, bicycle safety is an important yet complex problem, one that does not have a simple yes/no answer. To gain a better understanding, we need to take a variety of perspectives. Traditional bicycle safety analyses take a transportation engineering perspective by using distance-based exposure metrics or those based on the number of trips. But there are other perspectives – such as public health (using population) and transportation planning (using bicycle participation) – that could provide us with a fuller picture. This paper applies four measures of bicycling exposure to take a broad, interdisciplinary perspective on bicycling safety to answer the question of whether bicycling is actually getting safer and, if so, for whom. We first explore age differences with respect to the direction and magnitude of bicyclist fatality rates in the United States between 1985 and 2017. We then investigate whether any safety benefits are shared equally among commuters and recreational riders.Using fatality data from the Fatality Analysis Reporting System (FARS) and exposure data from the National Household Travel Survey (NHTS), the American Community Survey (ACS), the U.S. Census, and the National Sporting Goods Association (NSGA) – a relatively novel approach to measuring exposure through a comprehensive national bicycling participation survey – we derived trends of age-specific bicyclist fatality rates and corresponding confidence intervals for children and adults. We also compare exposure to fatality outcomes by deriving Pearson's correlation coefficients. This is the first research to provide a longitudinal, age-specific statistical analysis of bicycle fatality rates using these four exposure metrics.Results suggest that overall declines in bicycle fatality rates have been primarily driven by a sharp decline in child bicyclist fatalities while adult bicycle fatality rates have generally trended upwards (especially for the general population) or remained stagnant (for commuters). This work adds to the understanding of bicyclist safety and gives direction to future research regarding the importance of considering age and exposure sources.

An examination of relationships between cannabis legalization and fatal motor vehicle and pedestrian-involved crashes

Objective While attention has been given to how legalization of recreational cannabis affects traffic crash rates, there was been limited research on how cannabis affects pedestrians involved in traffic crashes. This study examined the association between cannabis legalization (medical, recreational use, and recreational sales) and fatal motor vehicle crash rates (both pedestrian-involved and total fatal crashes). Methods We used crash data from the Fatality Analysis Reporting System (FARS) to calculate monthly rates of fatal motor vehicle crashes and fatal pedestrian-involved crashes per 100,000 people from 1991 to 2018. Changes in monthly crash rates in three states that had legalized cannabis (Colorado, Washington, and Oregon) were compared to matched control states using segmented regression with autoregressive terms. Results We found no significant differences in pedestrian-involved fatal motor vehicle crashes between legalized cannabis states and control states following medical or recreational cannabis legalization. Washington and Oregon saw immediate decreases in all fatal crashes (-4.15 and −6.60) following medical cannabis legalization. Colorado showed an increase in trend for all fatal crashes after recreational cannabis legalization and the beginning of sales (0.15 and 0.18 monthly fatal crashes per 100,000 people). Conclusions Overall findings do not suggest an elevated risk of total or pedestrian-involved fatal motor vehicle crashes associated with cannabis legalization.

Analysis of Roadway Fatal Accidents using Ensemble-based Meta-Classifiers

In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) data set using several data mining algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.

United States Pedestrian Fatality Trends, 1977 to 2016

After decreasing for three decades, U.S. pedestrian fatalities increased by more than 40% between 2009 and 2016, hindering progress toward a future transportation system that produces zero deaths. While many researchers have investigated changes in the last decade, this study takes a long-term perspective and asks: what are the most common characteristics associated with U.S. pedestrian fatalities, and how have these characteristics shifted over the last 40 years? It analyzes all 231,675 pedestrian fatalities recorded between 1977 and 2016 in the Fatality Analysis Reporting System (FARS) database. Over 40 years, most pedestrian fatalities occurred in darkness (65%) and involved male pedestrians (70%) and male drivers (67%). They were commonly in roadway lanes (90%), away from intersections (80%), and involved vehicles traveling straight (83%). Most occurred on roadways with speed limits of 35 mph (56 km/h) or higher (70%) and four or more lanes (50%). Trends were compared across eight 5-year periods. Between the earliest and latest periods, there were significant decreases in the proportion of pedestrian fatalities among children younger than 15 (from 18% to 5%) and involving drivers who were drinking (from 15% to 8%). There were significant increases in pedestrian fatalities during darkness (from 63% to 73%), involving large vehicles (e.g., pickup trucks, vans, and SUVs) (from 22% to 44%), on roadways with speed limits 35 mph or higher (from 60% to 76%), and on roadways with four or more lanes (from 41% to 58%). These findings underscore the need for fundamental transportation systems changes to ultimately eliminate pedestrian fatalities.

Replicating the Levitt and Porter estimates of drunk driving

SummaryEstimates of the prevalence and risk of drinking‐and‐driving are a high‐priority need for researchers and policymakers. Levitt and Porter (Journal of Political Economy, 2001, 109(6), 1198–1237) demonstrate how these can be recovered using publicly available information in the Fatality Analysis Reporting System (FARS). Although robust to systematic misreporting and sample selection and far cheaper to implement than surveys, their methodological innovations are largely ignored. We believe this arises partly from difficulty in replicating their results. This article identifies the underlying causes of replication failure and offers practical guidance for future implementation that takes advantage of the current structure of the FARS data.

JA:2021-10. The Epidemiology of All-Terrain-Vehicle Injuries: 2015-2017

ABSTRACT Purpose: Injuries related to the operation of off-road vehicles (ORVs), including all-terrain vehicles (ATVs), continue to be a significant public health concern, especially in rural areas and agricultural production. In fact, a previous study of ATV-related occupational deaths utilizing the Bureau of Labor Statistics’ annual Census of Fatal Occupational Injuries found 61% were in the agriculture production industry, and the ATV-related fatality rate per million workers in agriculture was 143 times greater than all other industries. This study was designed to describe the characteristics of ATV injuries and fatalities by using three different national databases. Methods (if applicable): Data on ATV-related injuries and fatalities during the years 2015–2017 was obtained from the Fatality Analysis Reporting System (FARS), AgInjuryNews.org (AIN), and Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS). Results/Findings: We are currently analyzing the data for this ongoing study. A preliminary analysis was performed on FARS, AIN, and NEISS for 2015–2017. By using FARS, we identified 1004 ATV roadway fatalities in 974 crashes. These crashes were mostly single-vehicle (76%) and not a collision with a motor vehicle (77%; overturn/rollover, fall from the vehicle, hit fixed object) crashes. There were 156 fatal and 114 non-fatal agriculture-related ATV injuries by using AIN dataset. Of these injuries, 46% were to youth aged 16 years or younger, and 25% of the victims were using ATV for farm work at the time of the incident. Using NEISS data, an estimated 325,096 ATV-related injuries were treated in U.S. emergency departments from 2015–2017. Almost 30% of these injuries were to youth aged 16 years or younger. Practical Application: By presenting the results from these three datasets, we are hoping to provide a more complete picture of the burden of ATV-related fatalities and injuries. This is much needed to effectively inform injury prevention efforts in rural areas and in agriculture.

Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes

The number of fatalities and severe injuries in large truck-related crashes has significantly increased since 2009. According to the safety experts, the recent increase in large truck-related crashes can be explained by the significant growth in freight tonnage all over the U.S. over the past few years. This notable freight-haul growth has allowed continuous day–night movement of freight on roads and highways, exposing the trucks to a greater number of potential crashes or near-crash scenarios. There are many ongoing research efforts that aim to identify the different factors that influence large truck crashes; however, further research with innovative approaches is still needed to better understand the relationship between crash-related factors. In this study, the project team applied taxicab correspondence analysis (TCA), a data-mining method known for dimension reduction, to large truck fatal crash data to investigate the complex interaction between multiple factors under a two-dimensional map. For this study, 6 years (2010–2015) of large truck fatal crash data from the Fatality Analysis Reporting System (FARS) were used. The study found five clusters of attributes that show patterns of association between different crash attributes such as two-lane undivided roadways, intersection types, posted speed limit, crash types, number of vehicles, driver impairment, and weather. The findings of this study will help the safety professionals, trucking industry, and policymakers to make decisions for safer road design, and improvement in truck driver training, and education.