Effects of spatial correlation in random parameters collision count-data models

Barua, Sudip; El-Basyouny, Karim; Islam, Md. Tazul

doi:10.1016/j.amar.2015.02.001

Cited by 40 publications

(10 citation statements)

References 71 publications

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“…One solution to account for unobserved heterogeneity across observations in crash frequency analysis is to adopt random parameters count data models (Lord and Mannering, 2010;Chen and Tarko, 2014;Venkataraman et al, 2014;Barua et al, 2015Barua et al, , 2016Coruh et al, 2015;Alarifi et al, 2017;Bhat et al, 2017;Chen et al, 2017;Rista et al, 2017). Compared to fixed parameters models assuming the same effects of factors on all observations, random parameters models can capture the observation-specific effects of factors on crash frequency and have also been widely applied in crash injury severity analyses (Russo et al, 2014;Zhao and Khattak, 2015, Behnood and Mannering, 2016, 2017a, 2017bNaik et al, 2016;Anderson and Hernandez, 2017;Fountas and Anastasopoulos, 2017;Seraneeprakarn et al, 2017) and crash rate analyses (Anastasopoulos, 2016).…”

Section: Introductionmentioning

confidence: 99%

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Liu

Zhao

et al. 2018

Analytic Methods in Accident Research

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Urban midblock crashes are influenced mainly by traffic operation and roadway geometric features. In this paper, 10-year crash data from 1,506 directional urban midblock segments in Nebraska were analyzed using the multivariate random parameters zero-inflated negative binomial model to account for unobserved heterogeneity produced by correlations across segments, correlations across crash collision types, excessive zero crashes, and over dispersion. The multivariate random parameters zero-inflated negative binomial model was superior to many common crash frequency models in terms of both goodness of fit and prediction accuracy. Compared with the multivariate fixed parameters zero-inflated negative binomial model, the multivariate random parameters zero-inflated negative binomial model identified fewer key influencing factors and revealed segment-specific effects of these factors on different crash types. It showed that the number of lanes, annual average daily traffic per lane, and segment length might have non-positive effects on crash frequencies. Segments with a speed limit of 45 mph had fewer crashes than did those with lower speed limits, and there were fewer crashes on the segments in Omaha than on those in Lincoln. It was also found that neither the presence of a shoulder, on-street parking, or one-way traffic, nor lane width had significant influences on crash frequencies. These findings are informative for transportation agencies to take correct and efficient measures to accommodate diverse transportation demands without reducing traffic safety. By contrast, the fixed parameters model produced results consistent with intuition, but the results were insufficient to provide actionable recommendations.

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Section: Introductionmentioning

confidence: 99%

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Liu

Zhao

et al. 2018

Analytic Methods in Accident Research

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“…The latter does not imply the certainty of perfectly safe roadway segments, but the possibility that over a period of time accidents may not occur on some roadway segments. In fact, numerous studies (Abdel-Aty and Radwan, 2000;Anastasopoulos et al, 2008;Geedipally and Lord, 2010;Anwaar et al, 2012;Mohammadi et al, 2014;Zou et al, 2014;Barua et al, 2015; for a detailed list, see Lord and Mannering (2010)) have presented a great amount of roadway segments of varying lengths (from a few hundred feet to several miles) with zero accidents over their corresponding finite study periods, ranging from a few months to multiple years. Even though these zero-accident roadway segments are not considered as very low accident risk segments, they form the basis to compare segments with (or with a large amount of) accidents and identify safety countermeasures.…”

Section: Introductionmentioning

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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“…can serve as an indicator to assess the 24 relative strength of spatial and unstructured variations in the estimated coefficients. Besides, if 25 there is no significant heterogeneity in k β , the 2 k  then displays a dispersion with the mean 26 of its posterior distribution lower than the standard deviation (Barua et al, 2015). In this case, 27 the regression slopes are better fitted as the fixed effects.…”

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confidence: 99%

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

Huang

et al. 2017

Accident Analysis & Prevention

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This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.

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Effects of spatial correlation in random parameters collision count-data models

Cited by 40 publications

References 71 publications

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

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

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

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