Identifying Patterns of Key Factors in Sun Glare-Related Traffic Crashes

Sun glare is one of the major environmental issues contributing to traffic crashes. Every year, many traffic crashes in the United States are attributed to sun glare. However, quantitative analysis of the influence of sun glare on traffic crashes has not been widely undertaken. This study used traffic crash narrative data for 7 years (2010–2016) from Louisiana to identify crash reports that provided evidence of drivers indicating sun glare as the primary contributing factor of the crashes. Additional geometry and traffic information was collected to identify the list of key crash-contributing factors. This study used cluster correspondence analysis to perform the data analysis. After performing several iterations, six clusters were identified that provided additional insight in relation to sun glare-related crashes. The six clusters are associated with mixed (business and residential) localities, intersection-related crashes on U.S. roadways, single-vehicle crashes on residential two-lane undivided roadways, curve-related crashes on parish roadways in residential localities, interstate-related crashes in open country localities, and curve-related crashes in open country localities. The findings of the current study can add insights to the ongoing safety analysis on sun glare-related crashes.

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Mass School Shootings in the United States: A Novel Root Cause Analysis Using Lay Press Reports

Clinical Pediatrics ◽

10.1177/0009922819873650 ◽

2019 ◽

Vol 58 (13) ◽

pp. 1423-1428 ◽

Cited By ~ 2

Author(s):

Chris A. Rees ◽

Lois K. Lee ◽

Eric W. Fleegler ◽

Rebekah Mannix

Keyword(s):

United States ◽

Environmental Factors ◽

Root Cause Analysis ◽

The United States ◽

School Shootings ◽

Contributing Factors ◽

Mass Shootings ◽

Cause Analysis ◽

Factors Associated ◽

Root Cause

School shootings comprise a small proportion of childhood deaths from firearms; however, these shootings receive a disproportionately large share of media attention. We conducted a root cause analysis of 2 recent school shootings in the United States using lay press reports. We reviewed 1760 and analyzed 282 articles from the 10 most trusted news sources. We identified 356 factors associated with the school shootings. Policy-level factors, including a paucity of adequate legislation controlling firearm purchase and ownership, were the most common contributing factors to school shootings. Mental illness was a commonly cited person-level factor, and access to firearms in the home and availability of large-capacity firearms were commonly cited environmental factors. Novel approaches, including root cause analyses using lay media, can identify factors contributing to mass shootings. The policy, person, and environmental factors associated with these school shootings should be addressed as part of a multipronged effort to prevent future mass shootings.

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Increasing Awareness and Knowledge among Adult Latinos regarding Sexually Transmitted Infections

Multicultural Learning and Teaching ◽

10.1515/mlt-2019-0004 ◽

2020 ◽

Vol 15 (2) ◽

Author(s):

Ijeoma S. Eze ◽

Margaret Brady ◽

Beth Keely

Keyword(s):

Condom Use ◽

Sexually Transmitted Infections ◽

Low Income ◽

Outpatient Clinic ◽

The United States ◽

Consistent Condom ◽

Contributing Factors ◽

Consistent Condom Use ◽

Sexually Transmitted ◽

Post Test

AbstractLatinos in the United States are disproportionately affected by Human Immunodeficiency Virus and sexually transmitted infections (STIs). They account for about 24 % of newly diagnosed AIDS cases each year and are two to three times more likely to develop STIs when compared with non-Latino whites. Limited knowledge of STIs has remained one of the contributing factors to the disparity. The purpose of this study was to implement an educational bundle and evaluate its effectiveness in increasing STI knowledge and consistent condom use among adult Latinos seen in an outpatient clinic that serves low income patients. A pre- and post-test design was used to evaluate changes in STI knowledge and consistency of condom use. The educational bundle consisted of YouTube STI-related videos, a pre-designed one to one educational session, provision of condoms and STI-related educational materials. Self-administered questionnaires were used to collect data at baseline and one week post implementation of the educational bundle. Forty-six Latinos (18 females and 28 males) ranging in ages 19 to 60 years participated in the project. The total mean STI knowledge and condom use frequency improved from pre-test (M = 1.67, M = 3.09) to post-test (M = 3.80, M = 5.33). Results of paired samples t-tests demonstrated statistically significant differences between total mean STI knowledge (t(45) = –15.48, p < 0.001) and condom use frequency (t(45) = –7.09, p < 0.001) at pre-test and post-testing. The educational bundle was effective in increasing STI knowledge and consistent condom use among adult Latinos in an outpatient clinic. Further study needs to be done to determine the long-term effect of this mode of education for this population.

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Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽

10.3390/safety7020032 ◽

2021 ◽

Vol 7 (2) ◽

pp. 32

Author(s):

Syed As-Sadeq Tahfim ◽

Chen Yan

Keyword(s):

Injury Severity ◽

Unobserved Heterogeneity ◽

The United States ◽

Clustering Methods ◽

Severe Injuries ◽

Traffic Crash ◽

Crash Data ◽

Boosted Decision Trees ◽

The Individual ◽

Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

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Analysis and Identification of Contributing Factors of Traffic Crashes in New York City

Transportation Research Procedia ◽

10.1016/j.trpro.2021.07.161 ◽

2021 ◽

Vol 55 ◽

pp. 1696-1703

Author(s):

Khaled Shaaban ◽

Mohamed Ibrahim

Keyword(s):

New York ◽

New York City ◽

York City ◽

Contributing Factors ◽

Traffic Crashes

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Cheese-Associated Outbreaks of Human Illness in the United States, 1973 to 1992: Sanitary Manufacturing Practices Protect Consumers

Journal of Food Protection ◽

10.4315/0362-028x-61.10.1405 ◽

1998 ◽

Vol 61 (10) ◽

pp. 1405-1407 ◽

Cited By ~ 78

Author(s):

SEAN F. ALTEKRUSE ◽

BABAGALEH B. TIMBO ◽

JOHN C. MOWBRAY ◽

NANCY H. BEAN ◽

MORRIS E. POTTER

Keyword(s):

Public Health Problem ◽

The United States ◽

Contributing Factors ◽

E Coli ◽

Salmonella Infections ◽

Consumer Safety ◽

Control And Prevention ◽

Control Step ◽

Pathogen Control ◽

Human Illness

To identify contributing factors for cheese-associated outbreaks, we reviewed all cheese-associated outbreaks of human illness reported to the Centers for Disease Control and Prevention (CDC) with onsets during 1973 to 1992. The infrequency of large, cheese-associated outbreaks was notable because such outbreaks had been a frequent public health problem before the mid-20th century. Of 32 reported cheese-associated outbreaks, 11 attributed to manufacturing errors caused most of the illnesses and hospitalizations and all 58 deaths. Important factors in these 11 outbreaks were manufacturing cheese with raw or improperly pasteurized milk and postpasteurization contamination. If current Food and Drug Administration sanitary requirements for cheesemaking had been met, these outbreaks would have been preventable. In two outbreaks of Salmonella infections, fewer than 10 Salmonella per 100 g of cheese were detected. In two outbreaks of Brucella infections, efforts to recover the pathogen from the implicated cheese were unsuccessful, emphasizing the inadequacy of end product testing for assuring consumer safety. Curing cheeses kills most bacteria present in cheeses; however, evidence from sources other than the CDC Foodborne Disease Outbreak Surveillance System suggests that curing alone may not be a sufficient pathogen control step to eliminate Salmonella, Listeria, and E. coli O157:H7 from cheese.

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Effects of body protection vests and experience levels in prevention of equestrian injuries

BMJ Open Sport & Exercise Medicine ◽

10.1136/bmjsem-2018-000426 ◽

2018 ◽

Vol 4 (1) ◽

pp. e000426 ◽

Cited By ~ 2

Author(s):

Sarah A Andres ◽

Adrienne M Bushau-Sprinkle ◽

Michael E Brier ◽

Yvette R Seger

Keyword(s):

The United States ◽

Safety Equipment ◽

Contributing Factors ◽

Experience Levels ◽

Equestrian Sports ◽

Risk Of Injury ◽

Report Data ◽

Cross Country ◽

Serious Injuries ◽

Show Jumping

ObjectivesTo investigate the risk reduction and benefit of wearing body protection/safety vests in equestrian sports.MethodsA comparison of equestrians wearing body protective vests and those not wearing vests was performed using incident report data of 718 participants in the United States Pony Clubs during 2011–2017. Data obtained included age, gender, certification level of member, type of activity, description of incident, description of injuries, what protective equipment was worn and other possible contributing factors.ResultsWhile wearing body protective vests when riding on the flat or for show jumping was not correlated with a decrease in injuries, wearing vests for cross country was correlated with decrease in reported injuries (p=0.036) and showed a trend towards a lower incident severity level (p=0.062). Wearing body protection during cross country reduced the relative risk of injury by 56%. While the volume of incidents varied with a rider’s experience level, the number of serious injuries did not appear to correlate with lesser equestrian experience.ConclusionsWhile equestrian sports are considered to have a certain degree of risk associated with them, there are ways to make them safer. Wearing safety equipment, such as helmets and body protection, and obtaining education and experience can lessen the chance of incurring serious injuries.

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INNOVATION POTENTIAL OF NATIONAL ECONOMIES: A COMPARATIVE ANALYSIS

Economics Profession Business ◽

10.14258/epb202146 ◽

2021 ◽

pp. 116-124

Author(s):

D. O. Tarasov ◽

I. N. Dubina

Keyword(s):

The United States ◽

Key Factors ◽

Innovation Development ◽

Correlation And Regression Analysis ◽

Brics Countries ◽

Innovation Potential ◽

The Russian Federation ◽

Innovation Economy ◽

National Economies ◽

Degree Of Innovation

Currently, with the expansion of technological innovations, the economy of the Russian Federation is being transformed. For this reason, it becomes necessary to assess its innovation potential and compare it with the level of innovation potential of other countries. The economies of the BRICS countries were selected for the study, and the United States was also considered as an example of a developed innovation economy. To assess the innovation potential of these countries, groups of indicators were identified that determine the degree of innovation development. The study used data from the Science and Technology section of the World Bank site and applied the methods of correlation and regression analysis. The results of the analysis make it possible to identify and compare the key factors in the development of innovation potential in the countries under consideration.

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Microscopic Estimation of Freeway Vehicle Positions from the Behavior of Connected Vehicles

10.31224/osf.io/5qe2b ◽

2020 ◽

Author(s):

Noah J. Goodall ◽

Brian L. Smith ◽

Byungkyu Brian Park

Keyword(s):

Traffic Signals ◽

The United States ◽

Traffic Information ◽

Connected Vehicles ◽

Connected Vehicle ◽

Travel Time Prediction ◽

High Penetration ◽

Initial Penetration ◽

Car Following Model ◽

Status Data

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

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