Analysis of Factors Affecting Injury Severity in Traffic Crashes on Arizona Tribal Lands

2019 ◽  
Vol 2673 (9) ◽  
pp. 345-354
Author(s):  
Emmanuel James ◽  
Brendan J. Russo
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
The United States ◽  
The State ◽  
Severe Injury ◽  
Federal State ◽  
Severe Injuries ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Tribal Lands

Reducing fatal and serious injuries sustained in traffic crashes on tribal lands is a priority of federal, state, and local agencies in the United States. In the state of Arizona, the proportion of fatal and severe injury crashes on several areas of tribal land are 4.0% higher compared with statewide statistics. There is a need to investigate why higher proportions of fatal and severe injuries are occurring on tribal lands to plan effective countermeasures aimed at improving traffic safety in these areas. This study presents an analysis of factors affecting injury severity in crashes occurring within five tribal reservations in the state of Arizona. Crash data were obtained from the Arizona Department of Transportation, and the analysis included data for 9,597 persons involved in traffic crashes on these tribal lands for the years 2010–2016. An ordered logit model with random parameters was estimated using this data to identify factors significantly associated with severe injury outcomes in the event of a crash on tribal lands. Several person-, vehicle-, roadway-, and environmental-related variables were found to impact injury severity. For instance, alcohol and safety device usage were significantly associated with severity outcomes. The results of this study have the potential to aid transportation agencies effectively plan strategies to reduce traffic crash injuries and fatalities on tribal lands, and potential countermeasures considering the 4Es of traffic safety (engineering, education, enforcement, and emergency medical services) are discussed.

Is This the Care We Need?: An Examination of Childcare Policy in Pennsylvania

Commonwealth ◽  
2017 ◽  
Vol 19 (2) ◽  
Author(s):  
Jennie Sweet-Cushman ◽  
Ashley Harden
Keyword(s):  
United States ◽  
Child Care ◽  
Current System ◽  
The United States ◽  
The State ◽  
Federal State ◽  
Women And Politics ◽  
Richard Nixon ◽  
State And Local ◽  
Childcare Policy

For many families across Pennsylvania, child care is an ever-present concern. Since the 1970s, when Richard Nixon vetoed a national childcare program, child care has received little time in the policy spotlight. Instead, funding for child care in the United States now comes from a mixture of federal, state, and local programs that do not help all families. This article explores childcare options available to families in the state of Pennsylvania and highlights gaps in the current system. Specifically, we examine the state of child care available to families in the Commonwealth in terms of quality, accessibility, flexibility, and affordability. We also incorporate survey data from a nonrepresentative sample of registered Pennsylvania voters conducted by the Pennsylvania Center for Women and Politics. As these results support the need for improvements in the current childcare system, we discuss recommendations for the future.

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

Safety ◽  
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.

scholarly journals Teori Hukum Pidana Minimalis dari Douglas Husak: Urgensi dan Relevansi

2021 ◽  
Vol 4 (1) ◽  
pp. 245-279
Author(s):  
Mahrus Ali ◽  
M. Arif Setiawan
Keyword(s):  
United States ◽  
Criminal Law ◽  
The United States ◽  
The State ◽  
Risk Prevention ◽  
Legal Philosophy ◽  
Federal State ◽  
Minimalist Theory ◽  
Severity Of Punishment ◽  
Douglas Husak

Douglas Husak has been widely known, especially in the United States and Europe, as a leading theorist who combines the disciplines of legal philosophy and criminal law. Most of his writings were directed at the use of the coercive means of the state through criminal law as minimum as possible. The minimalist theory of criminal law that he coined was motivated by the phenomenon of the increasing number of acts criminalized in the United States Federal State Law in which the majority related to offenses of risk prevention causing overcriminalization. To prevent this, criminal law must be placed as a last resort. The state’s decision to criminalize an act must pay attention to internal and external constraints. The first includes the nontrivial harm or evil constraint, the culpability of the actor, and the proportionality of punishment, while the second is related to the substantiality of the state’s authority to punish. The thought is relevant to be adopted in the criminalization policy in Indonesia, especially regarding the principle of the blameworthiness of conduct, the severity of punishment must weigh the dangerousness of the (actor) offenses, and criminalization should not be taken if other means are equally effective or even more effective to achieve the goal. Abstrak Douglas Husak dikenal luas terutama di Amerika Serikat dan Eropa sebagai teoretisi terkemuka yang menggabungkan antara disiplin filsafat hukum dan hukum pidana. Tulisan-tulisan Husak kebanyakan diarahkan pada penggunaan sarana koersif negara melalui hukum pidana seminimal mungkin. Teori hukum pidana minimalis yang dicetuskannya dilatarbelakangi fenomena semakin banyaknya perbuatan-perbuatan yang dikriminalisasi dalam undang-undang Negara Federal Amerika dan mayoritas terkait offenses of risk prevention sehingga menimbulkan kelebihan kriminalisasi. Untuk mencegahnya, hukum pidana harus ditempatkan sebagai sarana terakhir. Keputusan negara untuk mengkriminalisasi suatu perbuatan harus memperhatikan pembatas internal dan pembatas eksternal. Yang pertama meliputi sifat jahat dan dampak kerugian/kerusakan yang begitu serius dari dilakukannya suatu tindak pidana, kesalahan pembuat, dan proporsionalitas pidana; sedangkan yang kedua terkait substansialitas kewenangan negara untuk memidana. Pemikiran Husak relevan untuk diadopsi dalam kebijakan kriminalisasi di Indonesia terutama menyangkut prinsip ketercelaan suatu perbuatan, penetapan beratnya ancaman pidana mengacu pada seriusitas delik dan kesalahan pembuat, dan kriminalisasi tidak boleh ditempuh jika cara-cara lain sama efektif atau bahkan lebih efektif untuk mencapai tujuan.

scholarly journals Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

2020 ◽  
Vol 32 (1) ◽  
pp. 39-53
Author(s):  
Dalia Shanshal ◽  
Ceni Babaoglu ◽  
Ayşe Başar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Injury Severity ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Spatio Temporal ◽  
Using Data

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

scholarly journals Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

2020 ◽  
Vol 10 (5) ◽  
pp. 1675 ◽  
Author(s):  
Ciyun Lin ◽  
Dayong Wu ◽  
Hongchao Liu ◽  
Xueting Xia ◽  
Nischal Bhattarai
Keyword(s):  
Machine Learning ◽  
Traffic Safety ◽  
Rural Areas ◽  
Computational Cost ◽  
The United States ◽  
Crash Severity ◽  
Rural Roads ◽  
Factors Affecting ◽  
West Texas ◽  
Teen Drivers

Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.

scholarly journals Exploring Factors Impacting Paths of Left-Turning Vehicles from Minor Road Approach at Unsignalized Intersections

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqiang Zhang ◽  
Yuli Qi ◽  
Jun Chen
Keyword(s):  
Traffic Safety ◽  
Major Road ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Traffic Efficiency ◽  
Logistic Analysis ◽  
Unsignalized Intersections ◽  
Minor Road ◽  
Traffic Conflicts ◽  
Probability Prediction

At unsignalized intersections, left-turning vehicles from minor road approach are more likely to be involved in traffic conflicts and traffic crashes and are one of the most leading factors impacting traffic efficiency and capacity. The authors of the paper observed that some drivers behaved illegally and dangerously while performing left turns from minor road approach, resulting in abnormal trajectories at unsignalized intersections. By applying binary logistic analysis, a probability prediction model was developed to explore various factors affecting probability of normal path taken by drivers while turning left from minor road approach. Based upon the model, measures such as lowering running speeds of vehicles on major road or minor road and adding more lanes on minor road can be used to encourage more drivers to take normal vehicle paths, which is helpful for the improvement of traffic safety, efficiency, and capacity. Results of the paper can be used for the guidance of design and management of unsignalized intersections.

scholarly journals Spatial Equity Analysis of Nighttime Pedestrian Safety: Role of Land Use and Alcohol Establishments in Albuquerque, NM

2021 ◽  
pp. 036119812110302
Author(s):  
Benson Long ◽  
Nicholas N. Ferenchak
Keyword(s):  
Land Use ◽  
Past Research ◽  
The United States ◽  
Sociodemographic Factors ◽  
Severe Injury ◽  
Severe Injuries ◽  
Lighting Condition ◽  
Crash Data ◽  
Pedestrian Crashes ◽  
Pedestrian Collisions

The United States experienced a 53% increase in pedestrian fatalities between 2009 and 2018, with 2018 having a 3.4% increase from 2017. Of the 2018 pedestrian fatalities with known lighting conditions, 76% occurred in dark/nighttime conditions, with 50% occurring between 6:00 and 11:59 p.m. Despite past research exploring several contributing characteristics for nighttime pedestrian crashes, there is limited research that investigates the spatial aspects of land use attributes and sociodemographic factors. Have these nighttime pedestrian collisions been concentrated in certain land uses? Could an establishment with the capacity to serve alcohol invoke a greater risk of pedestrian crashes? Does sociodemographic status correlate with clustering for fatal crashes, severe crashes, or both? To better understand the spatial characteristics of the recent increase in pedestrian collisions, we analyzed crash data from Albuquerque, New Mexico for pedestrian fatalities and severe injuries from 2013 to 2018 relative to lighting condition, land use (with a focus on alcohol establishments), and race/ethnicity on the block group level. We used confidence intervals and Getis-Ord Gi* statistics to verify the statistical integrity of the trends. Findings suggested that pedestrian fatality and severe injury rates were higher within a quarter mile of bars at night and in areas with elevated concentrations of minority populations. Pedestrian fatality and severe injury hot spots appeared to have higher percentages of non-white residents, coupled with lower sidewalk coverage and more arterials or collectors.

scholarly journals Institutional Framework for Assessing Water Availability and Allocation

2020 ◽  
Author(s):  
Ralph Wurbs
Keyword(s):  
Water Management ◽  
Water Availability ◽  
Water Allocation ◽  
The United States ◽  
State Legislature ◽  
The State ◽  
Lessons Learned ◽  
Planning System ◽  
Federal State ◽  
Interstate Compacts

Effective water resources management requires assessments of water availability within a framework of complex institutions and infrastructure employed to manage extremely variable stream flow shared by numerous often competing water users and diverse types of use. The Water Rights Analysis Package (WRAP) modeling system is fundamental to water allocation and planning in the state of Texas in the United States. Integration of environmental flow standards into both the modeling system and comprehensive statewide water management is a high priority for continuing research and development. The public domain WRAP software and documentation are generalized for application any place in the world. Lessons learned in developing and implementing the modeling system in Texas are relevant worldwide. The modeling system combines: (1) detailed simulation of water right systems, interstate compacts, international treaties, federal/state/local agreements, and operations of storage and conveyance facilities; (2) simulation of river system hydrology; and (3) statistical frequency and reliability analyses. The continually evolving modeling system has been implemented in Texas by a water management community that includes the state legislature, planning and regulatory agencies, river authorities, water districts, cities, industries, engineering consulting firms, and university researchers. The shared modeling system contributes significantly to integration of water allocation, planning, system operations, and research.

GOVERNMENT FUNDING FOR AGRICULTURAL RESEARCH IN THE UNITED STATES

2021 ◽  
Vol 3 (197) ◽  
pp. 9-16
Author(s):  
V.N. Minat ◽  
Keyword(s):  
United States ◽  
Structural Changes ◽  
Agricultural Research ◽  
The United States ◽  
Government Funding ◽  
Agricultural Science ◽  
The State ◽  
Federal State ◽  
State Financing ◽  
Effective Development

The innovation-oriented effective development of US agriculture is determined by the quantity and quality of the implemented results of agricultural research, funded by the main funds holder – the state. The purpose of the study is to identify trends in government funding of agricultural science in the United States. The rationale for these trends is considered in the unity of the dynamics and structure of state financing of agricultural research in 1889–2019 and the system characteristics of this process as an economic phenomenon. Using the techniques of statistical-economic and abstract- logical methods of research, combined with a historical approach to the subject of study, the author obtained empirical results reflecting the dynamics of structural changes in the state financing of agricultural research in the United States in different time periods. The trends towards regionalization of agricultural science subsidies within the federal state are also identified.

Association of traumatic brain injury severity and time to definitive care in three low-middle-income European countries

2021 ◽  
pp. injuryprev-2020-044049
Author(s):  
Corinne Peek-Asa ◽  
Madalina Adina Coman ◽  
Alison Zorn ◽  
Nino Chikhladze ◽  
Serghei Cebanu ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Injury Severity ◽  
Road Traffic ◽  
Severe Injury ◽  
Severe Injuries ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Traumatic Brain Injury Severity

BackgroundLow-middle-income countries experience among the highest rates of traumatic brain injury in the world. Much of this burden may be preventable with faster intervention, including reducing the time to definitive care. This study examines the relationship between traumatic brain injury severity and time to definitive care in major trauma hospitals in three low-middle-income countries.MethodsA prospective traumatic brain injury registry was implemented in six trauma hospitals in Armenia, Georgia and the Republic of Moldova for 6 months in 2019. Brain injury severity was measured using the Glasgow Coma Scale (GCS) at admission. Time to definitive care was the time from injury until arrival at the hospital. Cox proportionate hazards models predicted time to care by severity, controlling for age, sex, mechanism, mode of transportation, location of injury and country.ResultsAmong 1135 patients, 749 (66.0%) were paediatric and 386 (34.0%) were adults. Falls and road traffic were the most common mechanisms. A higher proportion of adult (23.6%) than paediatric (5.4%) patients had GCS scores indicating moderate (GCS 9–11) or severe injury (GCS 0–8) (p<0.001). Less severe injury was associated with shorter times to care, while more severe injury was associated with longer times to care (HR=1.05, 95% CI 1.01 to 1.09). Age interacted with time to care, with paediatric cases receiving faster care.ConclusionsImplementation of standard triage and transport protocols may reduce mortality and improve outcomes from traumatic brain injury, and trauma systems should focus on the most severe injuries.

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