Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models

Zou, Yajie; Wu, Lingtao; Lord, Dominique

doi:10.1016/j.amar.2014.12.002

Cited by 34 publications

(15 citation statements)

References 44 publications

(49 reference statements)

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“…In the accident injury-severity frequency framework, where the frequency of property damage only, injuries, and fatalities are modeled simultaneously, overdispersion is expected (Shankar et al, 1998;Venkataraman et al, 2014;Zou et al, 2015). The latter is identified when the overdispersion parameter, a, is statistically significant, which in turn indicates the statistical superiority of the negative binomial over the Poisson process.…”

Section: Multivariate Zero-inflated Count Data Modelsmentioning

confidence: 99%

Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

Anastasopoulos

2016

Analytic Methods in Accident Research

126

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Section: Multivariate Zero-inflated Count Data Modelsmentioning

confidence: 99%

Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

Anastasopoulos

2016

Analytic Methods in Accident Research

126

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“…Previously, researchers have mainly investigated two types of animal-vehicle collision data (number of carcass removal and reported AVCs) [16,17]. In order to reduce the risk of AVCs and formulate effective countermeasures, transportation safety researchers have tried various statistical models to study the influence of quantitative explanation on AVCs [18], such as Poisson regression [19][20][21][22], Negative Binomial (NB) regression [23][24][25][26][27], Poisson-lognormal regression model [28], and Gamma regression model [29,30].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

Yang

Zou

Wu³

et al. 2019

Journal of Advanced Transportation

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Two common types of animal-vehicle collision data (reported animal-vehicle collision (AVC) data and carcass removal data) are usually recorded by transportation management agencies. Previous studies have found that these two datasets often demonstrate different characteristics. To accurately identify the higher-risk animal-vehicle collision sites, this study compared the differences in hotspot identification and the effect of explanation variables between carcass removal and reported AVCs. To complete the objective, both the Negative Binomial (NB) model and the generalized Negative Binomial (GNB) are applied in calculating the Empirical Bayesian (EB) estimates using the animal collision data collected on ten highways in Washington State. The important findings can be summarized as follows. (1) The explanatory variables have different effects on the occurrence of carcass removal data and reported AVC data. (2) The ranking results from EB estimates when using carcass removal data and reported AVC data differ significantly. (3) The results of hotspot identification are different between carcass removal data and reported AVC data. However, the ranking results of GNB models are better than those of NB models in terms of consistency. Thus, transportation management agencies should be cautious when using either carcass removal data or reported AVC data to identify hotspots.

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“…To date, various models have been introduced to or developed for crash modeling analysis. For example, mixed-Poisson models[ 7 – 11 ], latent class/Markov switching models[ 12 – 17 ], random parameter models[ 18 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, in a simulation, it is possible to generate crash data with known regression parameters and an assumed distribution for error term. The simulation analysis have been adopted in some previous transportation safety studies[ 7 , 11 ] to evaluate the performance of different estimators. To complement outputs from simulation studies, crash data collected in California of USA are also used to compare the estimation results between the Poisson-SNP model and NB model.…”

Section: Introductionmentioning

confidence: 99%

A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data

Wang

Zou

et al. 2018

PLoS ONE

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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%.

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Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models

Cited by 34 publications

References 44 publications

Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data

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