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 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.

Estimate of the Safety Effect of All-Way Stop Control Conversion in Washington, DC

2020 ◽  
Vol 2674 (7) ◽  
pp. 77-86
Author(s):  
Zuxuan Deng ◽  
Sergiy Kyrychenko ◽  
Taylor Lee ◽  
Richard Retting
Keyword(s):  
Negative Binomial ◽  
Statistical Significance ◽  
Negative Binomial Regression ◽  
Washington Dc ◽  
Left Turn ◽  
Binomial Regression ◽  
Pedestrian Crashes ◽  
Safety Effect ◽  
Vision Zero

This study evaluated safety effects associated with converting traditional stop control (TSC) to all-way stop control (AWSC) at 53 intersections in Washington, DC. The study utilized an observational treatment group and a randomly selected comparison group. Negative binomial regression modeling was used to estimate the effect of AWSC conversion on crash outcomes, control for confounding factors, and check its statistical significance. The study also examined potential covariates that could influence AWSC crash outcomes, such as the number of legs of the intersection and the functional classification of the intersecting roads. This study found an overall 36% reduction in all crashes and a 42% reduction in injury crashes associated with converting intersections from TSC to AWSC. In addition, the study revealed a statistically significant reduction in right-angle crashes along with a statistically significant increase in straight hit pedestrian crashes. For all the other collision types, including right turn, left turn, rear-end, sideswipes, and bicycle crashes, no statistically significant coefficients were found. With many “Vision Zero” cities considering increased use of AWSC to help achieve their safety goals, it is important to understand and communicate the safety effects of AWSC.

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.

Property Crime Clearance in Small Jurisdictions

2018 ◽  
Vol 43 (4) ◽  
pp. 477-493 ◽  
Author(s):  
Jeffrey J. Roth
Keyword(s):  
Negative Binomial ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
The United States ◽  
Police Departments ◽  
Clearance Rates ◽  
Residential Instability ◽  
Large Cities ◽  
Binomial Regression ◽  
Crime Clearance

Research on the factors that influence crime clearance rates has primarily studied violent crimes in large cities. However, property crimes are among the most commonly occurring and least frequently cleared offenses, and the majority of police departments in the United States serve small jurisdictions. Thus, this study undertook an examination of the predictors of clearance rates for burglary, larceny, and vehicle theft in a sample of agencies serving populations of 50,000 people or fewer. Independent variables included both policing factors (e.g., workload, funding, broken windows arrests) and social disorganization indicators (e.g., residential instability, poverty). Negative binomial regression analyses revealed variation in the significance of the predictors across the three crimes. Additionally, many predictors found to be influential in prior work were insignificant in this study, which suggests differences in the nature of crime clearance between large cities and smaller jurisdictions and a need for further research in this area.

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.

Longitudinal Analysis of Light Rail and Streetcar Safety in the United States

2020 ◽  
Vol 2674 (9) ◽  
pp. 83-95
Author(s):  
Abubakr Ziedan ◽  
Candace Brakewood
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Descriptive Statistics ◽  
Light Rail ◽  
Right Of Way ◽  
Binomial Regression ◽  
Using Data ◽  
American Cities

Many American cities have launched or expanded light rail or streetcar services recently, which has resulted in a 61% increase in light rail and streetcar revenue miles nationwide during the period 2006–2016. Moreover, light rail and streetcars exhibit higher fatality rates per passenger mile traveled compared with other transit modes. In light of these trends, this study explores light rail and streetcar collisions, injuries, and fatalities using data obtained from the National Transit Database. This study applies a two-part methodology. In the first part, descriptive statistics are calculated for light rail and streetcar collisions, injuries, and fatalities, and a comparative analysis of light rail and streetcars is performed. In the second part, multilevel negative binomial regression models are used to analyze light rail and streetcar collisions and injuries. Three key findings have emerged from this study. First, the results generally align with findings from prior studies that show the majority of light rail and streetcar collisions occur in mixed right-of-way or near at-grade crossings. Second, this analysis revealed an issue predominantly at stations: 42% of light rail injuries were people waiting or leaving. Third, suicide was the leading cause of light rail fatalities, which represents 28% of all light rail fatalities. The implications of this study are important for cities that currently operate these modes or are planning to introduce new light rail or streetcar service to improve safety.

Hate Groups and Muslim Population Changes in the Fifty States: Does the Presence of Muslims Encourage Hate Group Formation?

2018 ◽  
Vol 25 (2) ◽  
pp. 317-332
Author(s):  
Daniel Hummel
Keyword(s):  
Negative Binomial ◽  
Group Formation ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Group Status ◽  
Hate Groups ◽  
Muslim Population ◽  
Binomial Regression ◽  
Positive Correlations

The size of the Muslim population in the United States is growing, while the number of hate groups also continues to increase across the states. Based on the social dominance theory and group threat theory, there may be a link between these two dynamics as social dominators become concerned about their group status, i.e. white Christian males. This potential relationship is explored in this article. Although there were significant positive correlations between the number of hate groups and the size of the Muslim population, a panelled negative binomial regression with a number of relevant control variables found that there was not a significant relationship between these variables at the 0.05 probability level. The article further explores these findings and future research in this area.

Sign in / Sign up

Export Citation Format

Share Document