A Comparative Analysis of Factors Affecting the Frequency and Severity of Freight-Involved and Non-Freight Crashes on a Major Freight Corridor Freeway

2018 ◽  
Vol 2672 (34) ◽  
pp. 49-62 ◽  
Author(s):  
Samuel G. Taylor ◽  
Brendan J. Russo ◽  
Emmanuel James
Keyword(s):  
Los Angeles ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Impact ◽  
The United States ◽  
Environmental Data ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Crash Data ◽  
Severity Prediction

Traffic crashes cost society billions of dollars each year as a result of property damage, injuries, and fatalities. Additionally, traffic crashes have a negative impact on mobility, as they are a primary cause of non-recurring delay. With the Interstate 10 corridor between the ports of Los Angeles and Houston being one of the most vital links for goods movement across the United States, safety and mobility along this freeway, particularly for freight traffic, are of significant concern. This study, which utilized six years of crash data from the state of Arizona, explores factors affecting the frequency and severity of crashes along the Arizona portion of the I-10 corridor, with a particular focus on freight-related crashes. The safety performance along the I-10 is analyzed through the development of crash frequency and severity prediction models using integrated crash, roadway, traffic, and environmental data. Negative binomial and ordered logit models, with the incorporation of random parameters, were estimated to provide a detailed understanding of factors associated with freight-involved crashes and how they compare to non-freight crashes in terms of frequency and severity. The results showed that several roadway- crash-, vehicle-, and person-related variables were associated with the frequency and/or severity of crashes along the study corridor. These findings provide important insights which can be used to develop or plan countermeasures aimed at improving the safety and efficiency of freight travel, which may include new ITS technologies, and targeted educational and enforcement campaigns.

scholarly journals Negative Binomial Regression Model for Road Crash Severity Prediction

2018 ◽  
Vol 12 (4) ◽  
pp. 38 ◽  
Author(s):  
Hana Naghawi
Keyword(s):  
Prediction Model ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Crash Severity ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Crash Data ◽  
Severity Prediction

In this paper, the Negative Binominal Regression (NBR) technique was used to develop crash severity prediction model in Jordan. The primary crash data needed were obtained from Jordan Traffic Institute for the year 2014. The collected data included number and severity of crashes. The data were organized into eight crash contributing factors including: age, age and gender, drivers’ faults, environmental factors, crash time, roadway defects and vehicle defects. First of all, descriptive analysis of the crash contributing factors was done to identify and quantify factors affecting crash severity, then the NBR technique using R-statistic software was used for the development of the crash prediction model that linked crash severities to the identified factors. The NBR model results indicated that severe crashes decreased significantly as the age of both male and female drivers increased. They significantly decreased as the environmental conditions improved. In addition, sever crashes were significantly higher during weekdays than weekends and in the morning than in the evening. The results also indicated that sever crashes significantly increased as drivers have faults while driving. In addition, mirror and brake deficits were found to be the only factors among all possible vehicle deficits factors that contributed significantly to severe crashes. Finally, it was found that the results of the NBR model are in agreement with the descriptive analysis of the crash contributing factors.

Does Displaying Safety Messages on Dynamic Message Signs have Measurable Impacts on Crash Risk?

2021 ◽  
pp. 036119812110444
Author(s):  
Megat-Usamah Megat-Johari ◽  
Nusayba Megat-Johari ◽  
Peter T. Savolainen ◽  
Timothy J. Gates ◽  
Eva Kassens-Noor
Keyword(s):  
Negative Binomial ◽  
Driver Behavior ◽  
Field Research ◽  
Crash Risk ◽  
Traffic Crashes ◽  
Crash Data ◽  
Safety Issues ◽  
Dynamic Message Signs ◽  
Safety Message ◽  
Message Signs

Transportation agencies have increasingly been using dynamic message signs (DMS) to communicate safety messages in an effort to both increase awareness of important safety issues and to influence driver behavior. Despite their widespread use, evaluations as to potential impacts on driver behavior, and the resultant impacts on traffic crashes, have been very limited. This study addresses this gap in the extant literature and assesses the relationship between traffic crashes and the frequency with which various types of safety messages are displayed. Safety message data were collected from a total of 202 DMS on freeways across the state of Michigan between 2014 and 2018. These data were integrated with traffic volume, roadway geometry, and crash data for segments that were located downstream of each DMS. A series of random parameters negative binomial models were estimated to examine total, speeding-related, and nighttime crashes based on historical messaging data while controlling for other site-specific factors. The results did not show any significant differences with respect to total crashes. Marginal declines in nighttime crashes were observed at locations with more frequent messages related to impaired driving, though these differences were also not statistically significant. Finally, speeding-related crashes were significantly less frequent near DMS that showed higher numbers of messages related to speeding or tailgating. Important issues are highlighted with respect to methodological concerns that arise in the analysis of such data. Field research is warranted to investigate potential impacts on driving behavior at the level of individual drivers.

Remittance Behavior among Salvadoran and Filipino Immigrants in Los Angeles

1998 ◽  
Vol 32 (1) ◽  
pp. 97-126 ◽  
Author(s):  
Cecilia Menjívar ◽  
Julie DaVanzo ◽  
Lisa Greenwell ◽  
R. Burciaga Valdez
Keyword(s):  
United States ◽  
Los Angeles ◽  
Family Income ◽  
Country Of Origin ◽  
The United States ◽  
Individual Characteristics ◽  
Host Countries ◽  
Factors Affecting ◽  
English Classes ◽  
Remittance Behavior

This article analyzes the factors that influence remittance behavior (the decision to remit and the amount sent) in the host country of Filipino and Salvadoran immigrants, two groups with high rates of U.S.-bound migration and of remittances. Data for this study come from a multipurpose survey fielded in Los Angeles in 1991 and are analyzed using logistic regressions and OLS. Individual characteristics and financial ability to remit, motivation to migrate, personal investments in the United States, and family obligations in the home and in the host countries are hypothesized to affect remittance behavior. No differences by country of origin in the proportion who send remittances were found, but there were significant differences in the amount remitted. Some variables affect the two country-of-origin groups differently. The size of remittances sent by Salvadorans tends to be relatively insensitive to their characteristics compared with Filipinos. Filipinos’ remittances are more affected by age, family income, having taken English classes in the United States, and living alone than are the remittances of Salvadorans. For both groups, the most consistent factors affecting remittances are family income and the place of residence of close family members.

The State-Based Factors Affecting Inward FDI Employment in the U.S.

2014 ◽  
pp. 85-103
Author(s):  
Lucyna Kornecki ◽  
E. M. Ekanayake
Keyword(s):  
Direct Investment ◽  
Negative Impact ◽  
Positive Impact ◽  
The United States ◽  
Real Wages ◽  
Financial Flows ◽  
Factors Affecting ◽  
Per Capita ◽  
Major Factors ◽  
The U.S

The descriptive part of this research focuses on the latest trends in U.S. inward Foreign Direct Investment (FDI) and describes the U.S. inward FDI flows and stock as a percentage of Gross Domestic Product (GDP) and includes geographic and sectoral distribution of inward U.S. FDI. The important part of U.S. inward FDI profile relates to inward U.S. FDI employment and inward U.S. FDI financial flows, which include equity, reinvested earnings, and intercompany debt. The corporate players, Mergers and Acquisitions(M&A’s) and green field investment are discussed briefly. The empirical part of this research investigates state-based factors affecting the inward FDI employment among 50 states of the United States and is based on data collected by the Commerce Department’s Bureau of Economic Analysis (BEA). This study identifies several state-specific determinants of FDI employment. The results indicate that the major factors exerting positive impact on inward U.S. FDI employment are: real wages, infrastructure, unionization level, educational attainment, FDI stock, and manufacturing density. In addition, the results show that gross state product growth rate, real per capita taxes has negative impact on FDI employment.

scholarly journals The Problem of Illegal Migration from China to Russia and Solutions

2017 ◽  
Vol 4 (4) ◽  
pp. 110-116
Author(s):  
Xun Yuan
Keyword(s):  
Negative Impact ◽  
Far East ◽  
The United States ◽  
Complex Problem ◽  
Real Problem ◽  
Russian Society ◽  
Illegal Migration ◽  
Scientific Publications ◽  
Factors Affecting ◽  
Illegal Migrants

In the article raises the complex problem to describe the components of the phenomenon of Chinese illegal migration in theRussian Federation, to identify ways of solving problematic issues. The investigation of the interpretation and application of the term«illegal migration» in regulatory documents and scientific publications of the United States, European Union and Russia. Outlines the author’s understanding and classification of the term as applied to the Russian migration situation. Shows the scale and form of Chinese illegal migration in Russia, examines the factors that affect the actualization of this problem, which can be classified in two main groups: factors affecting the penetration of Chinese illegal immigrants in Russia, and the factors that attract the attention of Russian society to Chinese migration.Ambivalent results were made: on the one hand, Chinese migration, including its irregular component contributes to the solution of the problem of labor shortages in agriculture and construction, especially in the Far East and Eastern Siberia. On the other, Chinese illegal migrants to Russia, is а real problem, which to some extent has a negative impact on the economy and society of the Russian Federation. In addition, the article States that the Chinese migrants (including illegal) in some cases become victims themselves, and in Russian society continues to evolve xenophobia and negative attitudes towards migrants. The study is fixed and develops the idea that there are three major ways to combat illegal migration: 1) improving the legislative framework and the effectiveness of its enforcement, 2) international cooperation on a bilateral and multilateral basis and 3) the efficiency of law enforcement agencies in developing and carrying out special operatively-preventive events.

scholarly journals Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks

2021 ◽  
Vol 12 ◽  
Author(s):  
Cathy C. Westhues ◽  
Gregory S. Mahone ◽  
Sofia da Silva ◽  
Patrick Thorwarth ◽  
Malthe Schmidt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grain Yield ◽  
Prediction Models ◽  
Predictive Ability ◽  
The United States ◽  
Environmental Data ◽  
Gradient Boosting ◽  
Phenotypic Traits ◽  
Environmental Predictors ◽  
Prediction Problems

The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

Effects of Varying Dispersion Parameter of Poisson–Gamma Models on Estimation of Confidence Intervals of Crash Prediction Models

2008 ◽  
Vol 2061 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Srinivas Reddy Geedipally ◽  
Dominique Lord
Keyword(s):  
Confidence Intervals ◽  
Negative Binomial ◽  
Prediction Models ◽  
Motor Vehicle ◽  
Dispersion Parameter ◽  
Transportation Safety ◽  
Dispersion Parameters ◽  
Crash Data ◽  
Varying Dispersion ◽  
Using Data

In estimating safety performance, the most common probabilistic structures of the popular statistical models used by transportation safety analysts for modeling motor vehicle crashes are the traditional Poisson and Poisson–gamma (or negative binomial) distributions. Because crash data often exhibit overdispersion, Poisson–gamma models are usually the preferred model. The dispersion parameter of Poisson–gamma models had been assumed to be fixed, but recent research in highway safety has shown that the parameter can potentially be dependent on the covari-ates, especially for flow-only models. Given that the dispersion parameter is a key variable for computing confidence intervals, there is reason to believe that a varying dispersion parameter could affect the computation of confidence intervals compared with confidence intervals produced from Poisson–gamma models with a fixed dispersion parameter. This study evaluates whether the varying dispersion parameter affects the computation of the confidence intervals for the gamma mean (m) and predicted response (y) on sites that have not been used for estimating the predictive model. To accomplish that objective, predictive models with fixed and varying dispersion parameters were estimated by using data collected in California at 537 three-leg rural unsignalized intersections. The study shows that models developed with a varying dispersion parameter greatly influence the confidence intervals of the gamma mean and predictive response. More specifically, models with a varying dispersion parameter usually produce smaller confidence intervals, and hence more precise estimates, than models with a fixed dispersion parameter, both for the gamma mean and for the predicted response. Therefore, it is recommended to develop models with a varying dispersion whenever possible, especially if they are used for screening purposes.

scholarly journals Application of Discrete Regression Models for Analyzing K-8 Students Nonchronic Absenteeism in the United States

2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Khairul Islam ◽  
Tanweer J. Shapla
Keyword(s):  
United States ◽  
Regression Models ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
The United States ◽  
Chronic Absenteeism ◽  
Factors Affecting ◽  
National Crisis ◽  
Binomial Regression

Absenteeism is a national crisis in the United States, and must be addressed adequately at the early stages or at its onset, to prevent consequential disaster and burden due to absenteeism. A pervasive and persuasive nonchronic absenteeism results in chronic absenteeism, and causes severe damage to students’ life, schools and societies. While a good number of articles address various issues relating to chronic absenteeism, no evidence of research exists investigating nonchronic absenteeism. The aim of this article is to investigate factors affecting nonchronic absenteeism in K-8 students in the United States by applying discrete regression models. Initially, we investigate K-8 students nonchronic absenteeism discrepancies due to socio-demographic and parental involvement factors via descriptive analysis and then employ Poisson and negative binomial regression models for exploring significant factors of K-8 nonchronic absenteeism. The findings of this study will be of great use to stakeholders in developing appropriate incentive measures for reducing nonchronic absenteeism early and thereby reducing chronic absenteeism.

scholarly journals Multi-Level Crash Prediction Models Considering Influence of Adjacent Zonal Attributes

2019 ◽  
Vol 5 (3) ◽  
pp. 649
Author(s):  
Nemat Soltani ◽  
Mahmoud Saffarzadeh ◽  
Ali Naderan
Keyword(s):  
Multilevel Model ◽  
Negative Binomial ◽  
Prediction Models ◽  
Level Structure ◽  
Factors Affecting ◽  
Crash Prediction ◽  
Independent Variables ◽  
Multi Level ◽  
Mode Of Transport ◽  
Crash Prediction Models

This study investigates factors affecting accidents across transport facilities and modes, using micro and macro levels variables simultaneously while accounting for the influence of adjacent zones on the accidents occurrence in a zone. To this end, 15968 accidents in 96 traffic analysis zones of Tehran were analyzed. Adverting to the multi-level structure of accidents data, the present study adopts a multilevel model for its modeling processes. The effects of the adjacent zones on the accidents which have occurred in one zone were assessed using the independent variables obtained from the zones adjacent to that specific zone. A Negative Binomial (NB) model was also developed, and results show that the multilevel model that considers the effect of adjacent zones shows a better performance compared to the multilevel model that does not consider the adjacent zones’ effect and NB model. Moreover, the final models show that at intersections and road segments, the significant independent variables are different for each mode of transport. Adopting a comprehensive approach to incorporate a multi-level, multi-resolution (micro/macro) model accounting for adjacent zones’ influence on multi-mode, multi-segment accidents is the contribution of this paper to accident studies.

scholarly journals Relationships between Demographic Factors and Chronic Conditions with Disease Severities

2021 ◽  
Vol 18 (21) ◽  
pp. 11469
Author(s):  
Van Cuong Nguyen ◽  
Jungmin Park
Keyword(s):  
Chronic Conditions ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Demographic Factors ◽  
Severity Of Illness ◽  
The Elderly ◽  
The United States ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Safe Level

Disease severities are the outcomes of an inpatient visit classification that assigns a diagnostic related group, including risk of mortality and severity of illness. Although widely used in healthcare, the analysis of factors affecting disease severities has not been adequately studied. In this study, we analyze the relationships between demographics and chronic conditions and specify their influence on disease severities. Descriptive statistics are used to investigate the relationships and the prevalence of chronic conditions. To evaluate the influence of demographic factors and chronic conditions on disease severities, several multinomial logistic regression models are performed and prediction models for disease severities are conducted based on National Inpatient Sample data for 2016 provided by the Healthcare Cost and Utilization Project database in the United States. The rate of patients with a chronic illness is 88.9% and the rate of patients with more than two chronic conditions is 67.6%; further, the rate is 62.7% for females, 73.9% for males, and 90% for the elderly. A high level of disease severity commonly appears in patients with more than two chronic conditions, especially in the elderly. For patients without chronic conditions, disease severities show a lower or safe level, even in the elderly.

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