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A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data
by
Ye, Xin
, Zou, Yajie
, Lord, Dominique
, Wang, Ke
in
Accidents, Traffic - statistics & numerical data
/ Automobile Driving - statistics & numerical data
/ Civil engineering
/ Computer Simulation
/ Data Interpretation, Statistical
/ Data structures
/ Driving ability
/ Education
/ Empirical analysis
/ Empirical models
/ Engineering and Technology
/ Engineering research
/ Fuzzy logic
/ Generalized linear models
/ Goodness of fit
/ Heterogeneity
/ Humans
/ Laboratories
/ Mathematical models
/ Motor vehicles
/ Neural networks
/ Nonparametric statistics
/ Normal distribution
/ People and places
/ Physical Sciences
/ Poisson density functions
/ Regression Analysis
/ Regression models
/ Roads & highways
/ Rural areas
/ Statistical analysis
/ Statistics, Nonparametric
/ Studies
/ Traffic accidents
/ Traffic accidents & safety
/ Traffic control
/ Traffic engineering
/ Transportation planning
/ Transportation safety
/ Vehicles
2018
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A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data
by
Ye, Xin
, Zou, Yajie
, Lord, Dominique
, Wang, Ke
in
Accidents, Traffic - statistics & numerical data
/ Automobile Driving - statistics & numerical data
/ Civil engineering
/ Computer Simulation
/ Data Interpretation, Statistical
/ Data structures
/ Driving ability
/ Education
/ Empirical analysis
/ Empirical models
/ Engineering and Technology
/ Engineering research
/ Fuzzy logic
/ Generalized linear models
/ Goodness of fit
/ Heterogeneity
/ Humans
/ Laboratories
/ Mathematical models
/ Motor vehicles
/ Neural networks
/ Nonparametric statistics
/ Normal distribution
/ People and places
/ Physical Sciences
/ Poisson density functions
/ Regression Analysis
/ Regression models
/ Roads & highways
/ Rural areas
/ Statistical analysis
/ Statistics, Nonparametric
/ Studies
/ Traffic accidents
/ Traffic accidents & safety
/ Traffic control
/ Traffic engineering
/ Transportation planning
/ Transportation safety
/ Vehicles
2018
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A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data
by
Ye, Xin
, Zou, Yajie
, Lord, Dominique
, Wang, Ke
in
Accidents, Traffic - statistics & numerical data
/ Automobile Driving - statistics & numerical data
/ Civil engineering
/ Computer Simulation
/ Data Interpretation, Statistical
/ Data structures
/ Driving ability
/ Education
/ Empirical analysis
/ Empirical models
/ Engineering and Technology
/ Engineering research
/ Fuzzy logic
/ Generalized linear models
/ Goodness of fit
/ Heterogeneity
/ Humans
/ Laboratories
/ Mathematical models
/ Motor vehicles
/ Neural networks
/ Nonparametric statistics
/ Normal distribution
/ People and places
/ Physical Sciences
/ Poisson density functions
/ Regression Analysis
/ Regression models
/ Roads & highways
/ Rural areas
/ Statistical analysis
/ Statistics, Nonparametric
/ Studies
/ Traffic accidents
/ Traffic accidents & safety
/ Traffic control
/ Traffic engineering
/ Transportation planning
/ Transportation safety
/ Vehicles
2018
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A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data
Journal Article
A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data
2018
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Overview
This paper develops a semi-nonparametric Poisson regression model to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US. Motor vehicle crash frequency on rural highway is a topic of interest in the area of transportation safety due to higher driving speeds and the resultant severity level. Unlike the traditional Negative Binomial (NB) model, the semi-nonparametric Poisson regression model can accommodate an unobserved heterogeneity following a highly flexible semi-nonparametric (SNP) distribution. Simulation experiments are conducted to demonstrate that the SNP distribution can well mimic a large family of distributions, including normal distributions, log-gamma distributions, bimodal and trimodal distributions. Empirical estimation results show that such flexibility offered by the SNP distribution can greatly improve model precision and the overall goodness-of-fit. The semi-nonparametric distribution can provide a better understanding of crash data structure through its ability to capture potential multimodality in the distribution of unobserved heterogeneity. When estimated coefficients in empirical models are compared, SNP and NB models are found to have a substantially different coefficient for the dummy variable indicating the lane width. The SNP model with better statistical performance suggests that the NB model overestimates the effect of lane width on crash frequency reduction by 83.1%.
Publisher
Public Library of Science,Public Library of Science (PLoS)
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