Application of finite mixture of negative binomial regression models with varying weight parameters for vehicle crash data analysis

2013 ◽  
Vol 50 ◽  
pp. 1042-1051 ◽  
Author(s):  
Yajie Zou ◽  
Yunlong Zhang ◽  
Dominique Lord
Keyword(s):  
Data Analysis ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Finite Mixture ◽  
Crash Data ◽  
Vehicle Crash ◽  
Binomial Regression ◽  
Crash Data Analysis

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 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.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

2018 ◽  
Vol 37 (20) ◽  
pp. 3012-3026 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Shrabanti Chowdhury ◽  
Himel Mallick ◽  
Prithish Banerjee ◽  
Broti Garai
Keyword(s):  
Health Care ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Care Demand ◽  
Binomial Regression
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