Fatality Analysis Reporting System Research Articles

Unraveling the veil of traffic safety: A comprehensive analysis of factors influencing crash frequency across U.S. States

Abstract Traffic crashes are a prominent cause of fatalities around the world. This study focuses on the dynamics of traffic safety by analyzing factors influencing crash frequency during both nighttime and daytime conditions. Four years data derived from the Fatality Analysis Reporting System (FARS) database were analyzed statistically based on the negative binomial regression model, to identify the contributions of several factors affecting crash outcomes. The findings reveal significant related factors including the area type, roadway alignment, speeding-related factors, gender, number of lanes, grade, surface type, and weather conditions that contributed to the expected crash frequency during both daytime and nighttime conditions. Through quantitative analysis, the extent to which each factor contributes to the expected crash frequency was determined, offering effective insights for policymaking to boost roadway safety. The findings highlight the necessity of implementing targeted strategies to minimize the risk of crashes by creating safer road environments.

Alcohol and drug presence in traffic crash fatalities before and after the COVID-19 pandemic: Evaluation of the fatality analysis reporting system (FARS) and linked medical examiner-vital records data in Clackamas, Multnomah, and Washington County, Oregon, 2019–2021

Traffic fatalities, with and from increased risky behaviors (reduced seat belt use, increased impairment from licit and illicit substances), have been increasing, especially during the COVID-19 pandemic. Death certificates are a major source of epidemiologic data in the United States, but have known underreporting of drug and alcohol presence. The Fatality Analysis Reporting System (FARS) is one major source of data on fatal crashes with intoxication. This study links FARS data for three counties in Oregon (2019–2021) with local medical examiner and death certificate data (FARS source data) and compares their concordance with blood alcohol concentration and toxicology for three major drug classes by year. For drivers only, our study finds good concordance between FARS and its source data in 2019 but poor concordance in 2020. This discordance may impact future analysis of impaired crash deaths, and we list some suggestions for amelioration.

Exploring differences in injury severity between occupant groups involved in fatal rear-end crashes: a correlated random parameter logit model with mean heterogeneity

ABSTRACT Rear-end crashes are one of the most common crash types. Passenger cars involved in rear-end crashes frequently produce severe outcomes. However, no study investigated the differences in the injury severity of occupant groups when cars are involved as following and leading vehicles in rear-end crashes. Therefore, the focus of this investigation is to compare the key factors affecting the injury severity between the front- and rear-car occupant groups in rear-end crashes. First, data is extracted from the Fatality Analysis Reporting System (FARS) for two types of rear-end crashes, including passenger cars as rear-end and rear-ended vehicles. Significant injury severity difference between front- and rear-car occupant groups is found by conducting likelihood ratio test. Moreover, the front- and rear-car occupant groups are modeled by the correlated random parameter logit model with heterogeneity in means (CRPLHM) and the random parameter logit model with heterogeneity in means (RPLHM), respectively. This study provides an insightful knowledge of mechanism of occupant injury severity in rear-end crashes.

Impact Area and Speed Effects on Powered Two-Wheeler Crash Fatality and Injury Risk

<div>The primary objective of this study was to evaluate the fatality risk of powered two-wheeler (PTW) riders across different impact orientations while controlling for different opponent vehicle (OV) types. For the crash configurations with higher fatality rate, the secondary objective was to create an initial speed–fatality prediction model specific to the United States. Data from the NHTSA Crash Reporting Sampling System and the Fatality Analysis Reporting System from 2017 to 2020 was used to estimate the odds of the different possible vehicle combinations and orientations in PTW–OV crashes. Binary logistic regression was then used to model the speed–fatality risk relationship for the configurations with the highest fatality odds. Results showed that collisions with heavy trucks were more likely to be fatal for PTW riders than those with other OV types. Additionally, the most dangerous impact orientations were found to be those where the PTW impacted the OVs front or sides, with fatality odds, respectively, four and five times higher than when the OV rear-end was impacted. The high variability in the odds of different crash configurations suggests the importance of considering the impact orientation factor in future injury prediction models. The speed–fatality prediction models developed for head-on and side crashes could provide an initial tool to evaluate the effectiveness of advanced rider assistance systems and other safety countermeasures in the United States, particularly those that result in speed reductions.</div>

Analyzing the time-varying patterns of contributing factors in work zone-related crashes

Work zones are crucial for maintaining and enhancing road infrastructure, but they also pose a significant risk to traffic safety. Between 2016 and 2020, work zone-related crashes in the United States increased by 13%, highlighting the pressing need for effective safety measures. This study examines the factors that influence work zone crashes, including traffic control devices, geometric configurations, traffic operations, and human factors. The study also investigates how these factors vary by work zone type, day of the week, and time of day. To explore these temporal and spatial impacts, the study utilized five years of fatal crash data from the Fatality Analysis Reporting System (FARS) and applied association rules mining. The findings demonstrate that rear-end crashes and collisions with other vehicles are the primary contributing factors. Although some common patterns emerged in the association rules, the study revealed temporal instability, highlighting the importance of developing work zone-specific safety countermeasures. These findings will inform the development of targeted safety interventions and ultimately reduce the risk of work zone crashes.

How Much Do We Know about Low-Speed Vehicle and Golf Cart Communities and Crashes: A Case Study in Nocatee, Florida

Low Speed Vehicle (LSV)/Golf Cart (GC) communities are those modern residential areas that fully or partially allow LSVs/GCs to travel within the community. Whereas LSV/GC communities are increasing rapidly, the safety impacts of LSV/GC on the community have rarely been investigated. Also, no national uniform manuals are available for guiding the proper design of signs, pavement markings, and signals in the community. In this study, five years (2016–2020) of fatal crash data that involved LSV/GC were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database to: (1) provide an overview of LSV/GC fatal crashes in the United States, (2) discuss characteristics of LSV/GC fatal crashes, and (3) delineate significant contributing factors. A case study in a large LSV/GC community in Florida was conducted to identify current issues and recommend improvements on: (1) driving behavior of LSV/GC drivers, (2) LSV/GC-related traffic control devices, and (3) LSV/GC community planning. The findings can help national and state agencies make proactive decisions to enhance traffic safety and livability for LSV/GC communities.

Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Emergency vehicle crashes, involving police vehicles, ambulances, and fire trucks, pose a serious traffic safety concern causing severe injury and deaths to first responders and other road users. However, limited research is available focusing on the contributing factors and their interactions related to these crashes. This research aims to address this gap by 1) identifying patterns of emergency vehicle crashes based on severity levels in both emergency and non-emergency modes and 2) comparing the associations by response modes for the related fatal, nonfatal injury, and no-injury crashes. Two national crash databases, Fatality Analysis Reporting System (FARS) and Crash Report Sampling System (CRSS), were utilized for police-reported emergency vehicle crashes from January 2016 to February 2020. Association rule mining (ARM) was employed to reveal the association between factors that strongly contributed to these crashes. The generated rules were validated using the lift increase criterion (LIC). The results showed the complex nature of risk factors influencing the severity of emergency vehicle crashes. The fatal consequences of speeding with no seatbelt usage were evident for emergency mode, whereas none of these risky driving attributes was observed for non-emergency mode. In addition, the analysis identified the risk of fatal emergency vehicle crashes involving pedestrians in dark-lighted conditions in both response modes. Regarding nonfatal injury severity, angle collisions were more likely to occur at urban intersections during emergencies, while rear-end crashes were more frequent on segments with a posted speed limit of 40–45 mph during non-emergency incidents. The outcomes also revealed that the no-injury crashes involving fire trucks exhibited different patterns depending on the response mode. The findings of this study can guide in making effective strategies to improve safe driving behavior of first responders. The identified associations provide insights into the factors that can be controlled to ensure safe operation of emergency vehicles on the road.

How can front crash prevention systems address more police-reported crashes in the United States?

Government and consumer-information organizations can motivate automakers to address additional crash types through front crash prevention (FCP) testing programs. This study examined the current state of crashes potentially relevant to current and future FCP systems to provide a roadmap for the next crash types that vehicle testing programs in the United States should evaluate.Crash records from 2016 to 2020 were extracted from the Crash Report Sampling System (CRSS) and the Fatality Analysis Reporting System (FARS). Crashes were restricted to ones involving no more than two vehicles where the striking or path-intruding vehicle was a passenger vehicle and a vehicle defect was not coded. Percentages of police-reported crashes, nonfatal-injury crashes, and fatal crashes were computed for different crash types and circumstances.Rear-end and pedestrian crashes evaluated in existing FCP testing programs accounted for 27% of all police-reported crashes, 19% of nonfatal-injury crashes, and 18% of fatal crashes. The remaining crash types relevant to FCP accounted for 25% of police-reported crashes, 31% of nonfatal-injury crashes, and 23% of fatal crashes. A turning passenger vehicle crossing the path of an oncoming vehicle accounted for the largest proportion of the remaining police-reported (8%) and nonfatal-injury crashes (13%). Head-on crashes accounted for the largest proportion of remaining fatal crashes (9%). Most FCP-relevant police-reported crashes occurred on roads with a posted speed limit between 30 and 50 mph. Medium/heavy trucks were the crash partner in a disproportionate number of fatal head-on and rear-end crashes and motorcycles in a disproportionate number of fatal rear-end and turning crossing-path crashes. Fatal bicyclist and pedestrian crashes were overrepresented at night.The findings from this study indicate that testing organizations should evaluate FCP performance at higher speeds; with non-passenger vehicles and vulnerable road users; during the night; and in more complex head-on and turning crash scenarios to reduce crashes of all severities. Some of these conditions are currently assessed by other testing organizations and can be readily adopted by U.S. programs or possibly addressed with new approaches like virtual testing.

Blood alcohol concentration as a measure of risk among pedestrian fatalities in the U.S., 2016–2020

Objective In the United States, deaths among pedestrians have increased dramatically since 2009 relative to other vulnerable road users, with substance use described as an important risk factor. This study aimed to explore blood alcohol concentrations (BAC g/dL) among pedestrian fatalities in the United States between 2016 and 2020. Exploring the presence of alcohol among pedestrian cases will support targeted interventions designed to reduce risk. Methods This study utilized pedestrian fatality and alcohol screening data provided by the Fatality Analysis Reporting System (FARS). Logistic models were examined to identify statistical associations (ORs, 95% CI) by age, race, and sex relative to positive BAC exposure (BAC > 0.0 g/dL), mild exposure (BAC > 0.0 < .079 g/dL), moderate to severe alcohol exposure (BAC 0.08–.299 g/dL), and severe exposure (BAC ≥ 0.30 g/dL). Results Between 2016 and 2020, 33,375 pedestrian fatalities were reported to FARS with 75.1% of cases retained for analysis (n = 25,077). Fatalities were more likely to be White (69.3%), male (69.9%), and between 25-64 years of age (67.3%). 74.0% of fatalities were tested for alcohol, with 40.9% screening positive. Females, cases ≥ 75 years of age, and those identified as Asians reported the lowest odds of being positive for alcohol exposure. Conclusions Results suggest an ongoing threat to pedestrians due to alcohol consumption and that exposure odds vary by demographic characteristics. Unfortunately, analytical approaches to understanding the roles played by drugs and alcohol among vulnerable road users tend to be marginalized in the literature. Analytical, evidence-based investigations are needed to curtail the risk of pedestrian fatalities in the U.S.

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

How Many People Were Killed by Windblown Dust Events in the United States?

Abstract Windblown dust events, including dust storms and smaller blowing dust events, pose severe risks to public health and transportation safety. In the United States, the statistics of fatalities caused by dust events remains elusive. We developed a new dataset by merging dust fatality data from NOAA Storm Events Database and the Department of Transportation Fatality Analysis Reporting System (FARS). There was a total of 232 deaths from windblown dust events from 2007 to 2017. This number is much larger than that reported by the NOAA Natural Hazard Statistics, which assigns some dust fatalities to high winds and thunderstorms (∼45%) and does not include many events in FARS. Dust fatalities are most frequent over the Southwest, consistent with the spatial distribution of dust storm occurrences. Other high-risk regions include the Colorado Plateau, Columbia Plateau in Washington and Oregon, the High Plains where the disastrous “Dust Bowl” occurred, and the Corn Belt where blowing dust from croplands presents a driving hazard. All six most deadly dust wrecks (three deaths or more) involved semi trucks and five of them were caused by dust storms along Interstate 10. There exist two “hotspots” for dust fatalities: 1) the “Deadliest 10 Miles” between Phoenix and Tucson, Arizona, and 2) Lordsburg Playa in New Mexico, where active dust mitigation projects have been managed by state transportation agencies. In most years, dust events caused comparable life losses to that from other weather hazards such as hurricanes, thunderstorms, lightning, and wildfires. This work presents new evidence that dust is an underappreciated weather hazard.

Revisiting the effect of recreational marijuana on traffic fatalities

This study examines the effect of retail recreational marijuana legalization on traffic fatalities using the most current data available and recent advancements in difference-in-difference estimation methods proposed by Callaway and Sant'Anna, (2021). A modified difference-in-difference (CS-DID) is used to estimate the effect of recreational marijuana legalization on traffic fatalities reported in the Fatality Analysis Reporting System (FARS). Difference-in-difference regression models are run at the state-year level, using data from 2007 through 2020, and compared to estimates using traditional two-way-fixed-effects (TWFE) models. Consistent with past studies, results from conventional TWFE suggest traffic fatalities increase at a rate of 1.2 per billion vehicle miles traveled (BVMT) after retail of recreational marijuana begins. However, using the CS-DID model, we find slightly larger average total treatment effects (∼2.2 fatalities per BVMT). Moreover, the size of the effect changes across time, where cohorts "treated" earlier have substantially higher increases than those who more recently legalized. Traffic fatalities increase by 2.2 per billion miles driven after retail legalization, which may account for as many as 1400 traffic fatalities annually. States who legalized earlier experienced larger traffic fatality increases. TWFE methods are inadequate for policy evaluation and do not capture heterogeneous effects across time.

Female crash fatality risk relative to males for similar physical impacts

Objective This paper estimates the latest trends regarding the effect of sex on driver-side or right-front passenger-side occupant fatalities in fatal crashes. The focus is on how recent model year (MY) light vehicles with advanced occupant protection technologies compare to older vehicles without these protections in terms of female fatality risk relative to males. Methods Data from the Fatality Analysis Reporting System (FARS) from 1975 to 2019 is used to calculate estimates of female fatality risk relative to males using logistic regression models and the double-pair comparison method. These estimates are calculated in various groups of MY vehicles and occupant protection systems. Occupant protection systems observed are the use of seat belts, and the availability of dual air bags, pretensioners and load limiters in the vehicles. All vehicles used include occupants age 16 to 96. Occupants studied include front-row occupants (drivers and right-front (RF) passengers). The average between drivers and RF passengers is also calculated. Results Incremental female fatality risk estimates versus males are reduced in newer MY vehicles. Incremental relative risks for female front-row occupants (average of drivers and right-front passengers) are found to be 19.9% (± 1.3) in fatal crashes in MY 1960–1999 vehicles, and 9.4% (± 2.2) percent in MY 2000–2020 vehicles. The difference in fatality risk between females and males is further reduced when looking at MY 2010–2020 vehicles (6.3 ± 5.4%) compared to MY 1960–2009 vehicles (18.3 ± 1.2%). Incremental fatality risk estimates also decrease in vehicles with newer generations of occupant protection systems regardless of MY. When occupants are belted in vehicles with the latest generation of modern occupant protection systems (dual air bags, seat belt pretensioners and load limiters), the estimated female fatality risk relative to males (average of drivers and right-front passengers) becomes 5.8% (± 3.8), which is lower than for belted occupants in vehicles without those occupant protections. Conclusions The disparity in fatality risk between males and females for the same physical impacts is reduced for later MY vehicles, as well as vehicles with more advanced occupant protection systems.

Driving under the influence of substances and motor vehicle fatalities among older adults in the United States

Objective This study examines contribution of substance use (including alcohol, cannabinoids, stimulants, narcotics, depressants, and hallucinogens) on the probability of drivers being at-fault for a crash on U.S. public roads, with specific emphasis on older adult drivers. Methods Data from the National Highway Traffic Safety Administration’s Fatality Analysis Reporting System (FARS) for the years 2010–2018 were employed for 87,060 drivers (43,530 two-vehicle crash pairs) involved in two moving vehicle crashes. The quasi-induced exposure (QIE) method was used to compute the relative crash involvement ratios (CIRs) for each relevant substance and illicit drug. Mixed-effect generalized linear regression models were fit to examine the effect of substance use on the probability of a driver being at-fault for a crash. Results There were 75.51% males and 73.88% Non-Hispanic Whites in our sample. The CIR for those aged 70–79 years was 1.17, and more than double (2.56) for the ≥80 years old drivers, while being relatively low among drivers of ages 20 to 69. Substance use, in general, disproportionately increased the probability of being at-fault during a crash, regardless of driver’s age. Though older drivers are less likely than other age groups to report substance use, presence of substances among older drivers increased the probability of their being at-fault two to four times during a crash across almost all substances. The regression models, after adjusting for driver’s sex, road grade, weather, light conditions, distraction, and speeding at time of crash, revealed that older drug-impaired drivers were twice as likely to be at fault in a fatal crash (aOR = 1.947; 95% CI = 1.821, 2.082; <0.0001) compared to their middle-aged counterparts. Similarly, most substance use categories were responsible for the probabilities of higher CIRs among the drivers. Conclusion These findings necessitate continued efforts to bring awareness to the deadly consequences of “drugged driving,” especially among older adult drivers.

Fatal Motor Vehicle Crashes in Upstate and Long Island New York: The Impact of High Visibility Seat Belt Enforcement on Multiple Risky Driving Behaviors.

Despite an observed daytime front-seat seat belt use that exceeds 90%, nearly half of motor vehicle occupants who die in New York State (NYS) each year are not wearing a seat belt. Crash outcomes were examined by occupant, vehicle, environmental and traffic enforcement patterns related to the annual Click It or Ticket high visibility seat belt enforcement campaign. Three periods of enforcement were examined: pre-enforcement, peri-enforcement (during/immediately after), and post-enforcement. Of the 14.4 million traffic citations, 713,990 (5.0%) were seat belt violations. Relative risk with 95% CI was assessed using deaths from the Fatality Analysis Reporting System (FARS) and SAS Glimmix 9.4 software. Mortality was lower peri-enforcement (32.9%) compared to pre- (40.9%) or post-enforcement (37.1%) (p < 0.001) and tended to be elevated in low enforcement response areas (43.6%). Fatalities were 30% lower (0.7, 95% CI 0.6−0.9) during peri-enforcement in models adjusted for demographics, law coverage, enforcement response, rural, weekend, impairment, speeding, and vehicle type. Adjusted mortality was higher in rural (1.9, 1.6−2.6), alcohol-involved (1.8, 1.4−2.9), and speeding-involved (2.0, 1.7−2.5) crashes. Peri-enforcement alcohol- and speed-involved fatalities tended to be lower in restrained, unrestrained and occupants missing belt status. The finding of lower mortality in both belted and unbelted occupant’s peri-enforcement—in the context of fewer fatal speed and alcohol-involved crashes—suggests that the mechanism(s) through which high visibility seat belt enforcement lowers mortality is through impacting multiple risky driving behaviors.

Reporting of unknown drug test results in the Fatality Analysis Reporting System and associated factors, 2000–2020

Objective To investigate how the percentage of unknown drug test results among drug-tested drivers in the Fatality Analysis Reporting System (FARS) has trended over the past 2 decades and to evaluate factors that may affect a drug-tested driver having unknown test results in FARS. Methods The percentage of unknown test results among fatally injured drivers who were tested for drugs in FARS was assessed from 2000 to 2020. Trends in annual FARS drug testing data were compared with those for alcohol testing. In addition, the percentage of unknown drug test results was regressed on several factors that have been shown to be associated with higher risk of drug-involved crash fatalities. Results The percentage of unknown drug test results in FARS has decreased drastically over the past 2 decades, and the percentage of unknown drug test data gradually matched that of alcohol data over the study period. Multiple factors such as the fatally injured drivers’ age and whether the crash occurred in an urban/rural area were found to be statistically significantly associated with the percentage of unknown drug test results in FARS. Conclusions The percentage of unknown test results in FARS drug data is decreasing, and the significant associated factors found in this study may help identify additional strategies for reducing unknown drug test results. Future research should focus on continued improvement of FARS data, given the importance of FARS in understanding fatal crashes and informing strategies for prevention of crash-related injuries and fatalities in the United States.

Analysis of Electric Vehicle Collisions in the United States: An Epidemiological Study.

Despite the increase in electric vehicle sales in the US, their impact on injuries and fatalities is still understudied. We aim to evaluate injuries and fatalities associated with electric vehicle collisions in the US. The study utilized electric vehicle injury and fatality data from the Fatality Analysis Reporting System (FARS). All electric vehicle models available within the FARS database and sold in the US from 2014 to 2020 were selected. Electric vehicle models were matched to analogous motor vehicles when possible. No significant increase in electric vehicle fatality per capita (FPC) was found during the study period (2014: .41 vs 2020: 1.42, per 100000 electric cars, P = .080). However, 82% of all fatalities occurred on non-intersectional local roadways with 46% occurring in the presence of speeding, 14% in the presence of fire, and 38% involving a driver with an elevated blood alcohol content (BAC). The Tesla Model S, Kia Niro, and Hyundai IONIQ accounted for the most fatality per capita (17.89 vs 10.27 vs 8.42, per 100000 electric cars). Upon comparison of electric vehicles to analogous motor vehicles produced within the same year, the Hyundai IONIQ had a significantly lower FPC compared to the Hyundai Elantra (7.33 vs 23.51, per 100000 electric cars P = .034). While no significant increase in electric vehicle fatality per capita (FPC) was found, the total number of electric vehicle fatalities did increase significantly during the study period (2014-2020). Furthermore, a significant proportion of these fatalities is directly related to speeding, fire, and intoxicated driving.

Proximity to fatal accidents predicts police citation rates on urban and rural roads

Objectives: Police enforcement can effectively deter risky driving behaviors and reduce traffic fatalities, including speed-related fatalities. Unlike other areas of data-driven policing, spatial methods to improve road safety are not well-described. The objectives of this study were as follows: (1) determine if proximity to a prior roadway fatality increases the risk of a traffic citation being issued after adjusting for relevant roadway variables; (2) determine if this effect varies between rural and urban roads. Methods: The study region included a rural county and adjacent small city (City of Charlottesville, Albemarle County, Virginia). Fatality locations were obtained from the Fatality Analysis Reporting System (FARS) from 2008 to 2018. Police citation data were obtained from the State of Virginia for 2020. Data on fatalities and roadway features were used to create a model to predict traffic citation density. Traffic stop locations were analyzed as a point pattern on a linear network, assuming a Poisson process with varying intensity. The model adjusted for average traffic volume, distance to the nearest fatal crash along the road network, rural vs urban roadway, posted speed limit, and interstate vs non-interstate road. To account for over-dispersion, quasi-Poisson model was used. Results: There were 138 fatalities and 651 traffic citations during the time periods examined. After adjusting for other covariates, the expected number of citations/km was higher with increasing proximity to prior fatal crashes, RR = 1.34 (95% CI: 1.04, 1.72) per km. The effect of proximity did not vary significantly between urban and rural roads (p = 0.2707). However, citation intensity was significantly higher on urban roads vs. rural roads, RR = 2.65 (1.09, 6.45). Predicted citation intensity reflected anticipated enforcement clusters inside the city limits and on major county roads, suggesting satisfactory model fit. Conclusions: This study demonstrated a novel approach to quantify the impact of road fatalities on police activity, measured by traffic citations. Proximity to fatal crashes was found to affect police citation rates, and this effect is consistent between urban and rural areas. Future work will aim to identify areas of under enforcement based on proximity to fatal crashes and other roadway variables.

Predictive analysis of injury severity of person across angle crashes using machine learning models

Human injury in a vehicle crash is a critical subject of analysis. The injury severity of a person in crashes helps the transportation agency to determine crash conditions. This will in turn helps road safety manager or engineer to implement the counter measures and to improve and enhance the level of safety at the roadside. In this research study, the injury severity of a person in crash analysis across angles is analyzed using various machine learning models. For the injury severity prediction 2018 Fatality Analysis Reporting System (FARS) NHTSA (National Highway Traffic Safety Administration) dataset of United States is used. The person injury severity is predicted with the help of machine learning models like Multinomial logistic regression, Naive Bayes Classifier, Random Forest, Extra Trees, XGB Classifier and optimized XGBoost model. It is observed that optimized XGBoost method performs better than other machine learning models in terms of performance metrics like Accuracy, Error rate, Cohen-kappa-score, Loss and Misclassified samples.

Relationship of pedestrian crash types and passenger vehicle types

Introduction: This is the first known study that examines the association between common pedestrian crash types and passenger vehicle types. Method: The analysis included single-vehicle, single-pedestrian crashes from two data sets: North Carolina state crash data and the Fatality Analysis Reporting System (FARS). We performed separate multinomial logistic regression analyses of major pedestrian crash types occurring at or near intersections and at nonintersections. Results: At or near intersections, minivans, large vans, pickups, and SUVs (collectively known as light truck vehicles, or LTVs) were more likely than cars to be involved in crossing-roadway–vehicle-turning-left crashes versus crossing-roadway–vehicle-not-turning crashes. LTVs were also more likely involved in fatal crossing-roadway–vehicle-turning-right crashes at or near intersections versus crossing-roadway–vehicle-not-turning crashes when compared with cars. At nonintersections, LTVs were associated with increased odds of walking-along-roadway crashes relative to crossing-roadway–vehicle-not-turning crashes when compared with cars. Conclusions: LTVs were more likely to be involved in certain pedestrian crash types, implying a potentially problematic visibility of pedestrians near the front corners of these vehicles. Practical applications: More research is needed to examine A-pillar blind zones by vehicle type. If it is found that LTVs have larger blind zones, automakers should consider ways to design the A-pillars of these vehicles to minimize blind zones while maintaining pillar strength. Doing this could improve pedestrian safety around these increasingly popular larger vehicles.