scholarly journals Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

scholarly journals Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Yopi Ariesia Ulfa ◽  
Agus M Soleh ◽  
Bagus Sartono
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Directorate General ◽  
The Republic ◽  
And Control

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.

Effects of Ramp Spacing on Freeway Mainline Crashes

2011 ◽  
Vol 97-98 ◽  
pp. 95-99
Author(s):  
Yong Qing Guo
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Model Estimation ◽  
Vehicle Crashes ◽  
Crash Data ◽  
Binomial Regression ◽  
Single Vehicle ◽  
Exit Ramp

This research applies Negative Binomial regression models to investigate safety effects of ramp spacing. Data for model estimation was collected in 112 freeway segments where each entrance ramp is followed by an exit ramp. Three years (2005-2007) of freeway crash data were examined by the NB model in this study. The modeling results suggest that the frequencies of total crashes, fatal-plus-injury crashes, single-vehicle crashes and multiple-vehicle crashes increase as ramp spacing decreases, and the frequencies of total crashes and multiple-vehicle crashes increase at significant rates. The modeling result has been geared into the development of accident modification factors (AMFs) for ramp spacing that can be used safety prediction of freeways.

scholarly journals Spatiotemporal Convergence of Crime and Vehicle Crash Hotspots: Additional Consideration for Policing Places

2017 ◽  
Vol 64 (14) ◽  
pp. 1795-1819 ◽  
Author(s):  
Jeremy G. Carter ◽  
Eric L. Piza
Keyword(s):  
Negative Binomial ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Additional Consideration ◽  
Relationship Strength ◽  
Crash Data ◽  
Vehicle Crash ◽  
Binomial Regression ◽  
Crash Hotspots

Policing strategies that seek to simultaneously combat crime and vehicle crashes operate under the assumption that these two problems have a corollary relationship—an assumption that has received scant empirical attention and is the focus of the present study. Geocoded vehicle crash, violent crime, and property crime totals across were aggregated to Indianapolis census blocks over a 36-month period (2011-2013). Time series negative binomial regression and local indicators of spatial autocorrelation analyses were conducted. Results indicate that both violent and property crime are significantly related to vehicle crash counts, both overall and during the temporal confines of patrol tours. Relationship strength was modest. Spatiotemporal analysis of crime and crash data can identify places for police intervention and improved scholarly evaluation.

scholarly journals Count Regression Models for Analyzing Crime Rates in The East Java Province

2021 ◽  
Vol 2123 (1) ◽  
pp. 012028
Author(s):  
Dian Handayani ◽  
A F Artari ◽  
W Safitri ◽  
W Rahayu ◽  
V M Santi
Keyword(s):  
Poisson Regression ◽  
Crime Rate ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Crime Rates ◽  
Poor People ◽  
Explanatory Variables ◽  
Generalized Poisson ◽  
Binomial Regression ◽  
Generalized Poisson Regression

Abstract Crime rate is the number of reported crimes divided by total population. Several factors could contribute the variability of crime rates among areas. This study aims to model the relationship between crime rates among regencies and cities in the East Java Province (Indonesia) and some potentially explanatory variables based on Statistics Indonesia publication in 2020. The crime rate in the East Java Province was consistently at the top three after DKI Jakarta and North Sumatra during 2017 to 2019. Therefore, it is interesting for us to study further about the crime rate in the East Java. Our preliminary analysis indicates that there is an overdispersion in our sample data. To overcome the overdispersion, we fit Generalized Poisson and Negative Binomial regression. The ratio of deviance and degree of freedom based on Negative Binomial is slightly smaller (1.38) than Generalized Poisson (1.99). The results indicate that Negative Binomial and Generalized Poisson regression, compared to standard Poisson regression, are relatively fit to model our crime rate data. Some factors which contribute significantly (α=0.05) for the crime rate in the East Java Province under Negative Binomial as well as Generalized Poisson regression are percentage of poor people, number of households, unemployment rate, and percentage of expenditure.

Predicting Dengue Hemorraghic Fever Cases in Metro Manila, Philippines

2020 ◽  
Author(s):  
Imee Necesito ◽  
Jaewon Jung ◽  
Young Hye Bae ◽  
Soojun Kim ◽  
Hung Soo Kim
Keyword(s):  
Principal Components ◽  
Negative Binomial ◽  
Moving Average ◽  
Principal Component ◽  
Negative Binomial Regression ◽  
The Philippines ◽  
Dengue Case ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Metro Manila

<p>Researchers have been looking for methods to prevent, control and provide lifelong protection to humans against dengue disease which is brought by the dengue-carrying mosquito called the Aedes Aegypti. However, such prevention, control and protection will best be aided by a dengue case prediction model. This study used the Negative Binomial Regression to forecast the dengue case incidence in Metro Manila, Philippines using principal components as explanatory variables. To ensure that the dengue cases are predictable, close returns plot (CRP) was performed.   The logarithm of dengue case incidence which were assigned as response variables have showed higher value of variance over the mean which validates the use of negative binomial regression. Principal Component Analysis utilizing Nino 3.4 sea surface temperature (SST), precipitation and minimum temperature was used in the study. The acquired principal components (PC1, PC2, PC3 and PC4) were used as the explanatory variables for the negative binomial regression to calculate the number of the logarithm of dengue case incidence. However, to improve the calculated value of DHF cases in comparison to its actual value, residuals from the negative binomial regression were treated using moving average approach. The data used in this study were from 1994-2010 climatological data. Results for both negative binomial and moving average were combined to get the forecasted dengue incidence. Forecasted values showed a maximum of 12% difference from the actual DHF cases indicating a high forecasting performance. This study which focused on predicting the possible dengue incidence in the central districts of the Philippines  is believed to be essential to create plans of action to prevent and control this disease.</p>

Identification of Critical Intersection Angle through Crash Modification Functions

2019 ◽  
Vol 2673 (2) ◽  
pp. 531-543 ◽  
Author(s):  
Wesley Kumfer ◽  
David Harkey ◽  
Bo Lan ◽  
Raghavan Srinivasan ◽  
Daniel Carter ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Skew Angle ◽  
Intersection Angle ◽  
Crash Data ◽  
Highway Safety Manual ◽  
Intersection Safety ◽  
Binomial Regression ◽  
Safety Effect

A significant portion of both fatal and total crashes occurs at intersections in the United States. Skew angle may be a significant contributor to these crashes. This paper examines the effects of intersection angle on intersection safety performance. With seven years of crash data from Minnesota and five years of crash data from Ohio, random forest regression data mining and negative binomial regression models were developed to estimate crash modification functions at three-leg and four-leg stop-controlled intersections with two-lane and multilane major legs. Where possible, the results were compared between the two states and used to develop average crash modification function curves. This study shows that over half of the intersection types experience the highest number of predicted crashes when the intersection angle between roadway legs is between 50 degrees and 65 degrees. These results have practical implications for engineers and safety professionals. First, the crash modification function curves supplement and revise the guidance for intersection angle in the Highway Safety Manual and Policy on Geometric Design of Highways and Streets. Second, the functions offer new guidance to agencies planning intersection improvements. Third, the crash modification functions can be used to determine the safety effect of changes in intersection angle.

scholarly journals Overdisp: A Stata (and Mata) Package for Direct Detection of Overdispersion in Poisson and Negative Binomial Regression Models

2020 ◽  
Vol 8 (3) ◽  
pp. 773-789
Author(s):  
Luiz Paulo Lopes Fávero ◽  
Patrícia Belfiore ◽  
Marco Aurélio dos Santos ◽  
R. Freitas Souza
Keyword(s):  
Count Data ◽  
Data Model ◽  
Regression Models ◽  
Negative Binomial ◽  
Direct Detection ◽  
Negative Binomial Regression ◽  
The Other ◽  
Explanatory Variables ◽  
Data Regression ◽  
Binomial Regression

Stata has several procedures that can be used in analyzing count-data regression models and, more specifically, in studying the behavior of the dependent variable, conditional on explanatory variables. Identifying overdispersion in countdata models is one of the most important procedures that allow researchers to correctly choose estimations such as Poisson or negative binomial, given the distribution of the dependent variable. The main purpose of this paper is to present a new command for the identification of overdispersion in the data as an alternative to the procedure presented by Cameron and Trivedi [5], since it directly identifies overdispersion in the data, without the need to previously estimate a specific type of count-data model. When estimating Poisson or negative binomial regression models in which the dependent variable is quantitative, with discrete and non-negative values, the new Stata package overdisp helps researchers to directly propose more consistent and adequate models. As a second contribution, we also present a simulation to show the consistency of the overdispersion test using the overdisp command. Findings show that, if the test indicates equidispersion in the data, there are consistent evidence that the distribution of the dependent variable is, in fact, Poisson. If, on the other hand, the test indicates overdispersion in the data, researchers should investigate more deeply whether the dependent variable actually exhibits better adherence to the Poisson-Gamma distribution or not.

scholarly journals Analysis of the correlation of socioeconomic, sanitary, and demographic factors with homicide deaths - Bahia, Brazil, 2013-2015

2020 ◽  
Vol 73 (6) ◽  
Author(s):  
Tiago Oliveira de Souza ◽  
Edinilsa Ramos de Souza ◽  
Liana Wernersbach Pinto
Keyword(s):  
Regression Models ◽  
Ecological Study ◽  
Negative Binomial ◽  
Demographic Factors ◽  
Negative Binomial Regression ◽  
Legal Intervention ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Education Levels ◽  
Using Data

ABSTRACT Objective: To analyze the correlation of socioeconomic, sanitary, and demographic factors with homicides in Bahia, from 2013 to 2015. Methods: Ecological study, using data from the Information System on Mortality and from the Superintendence of Economic and Social Studies. The depending variable is the corrected homicide rate. Explanatory variables were categorized in four axes. Simple and multiple negative binomial regression models were used. Results: Positive associations were found between homicides and the Index of Economy and Finances (IEF), the Human Development Index, the Gini Index, population density, and legal intervention death rates (LIDR). The variables Index of Education Levels (IEL), rates of death with undetermined intentions (RDUI), and the proportion of ill-defined causes (IDC) presented a negative association with the homicide rates. Conclusion: The specific features of the context of each community, in addition to broader socioeconomic municipal factors, directly interfere in life conditions and increase the risk of dying by homicide.

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