The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Wen, Huiying; Sun, Jiaren; Zeng, Qiang; Zhang, Xuan; Yuan, Quan

doi:10.1155/2018/6964828

Cited by 25 publications

(16 citation statements)

References 35 publications

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“…The freeware WinBUGS provides a friendly programming environment for conducting Bayesian inference on the parameters in the CAR model, which does not have an easily calculable likelihood function. Besides, the multivariate version of the CAR model is the state-of-the-art method for multivariate spatial modeling crash frequency by injury severity [22,23,24], crash type [25], transportation mode [26], or crash period [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

Wen

Zhang

Zeng

et al. 2019

IJERPH

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This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, the spatial autocorrelation and the spillover effects are formulated as the conditional autoregressive (CAR) prior and the exogenous variables of adjacent segments, respectively. The proposed model is demonstrated and compared to the models with only one kind of spatial effect, using one-year crash data collected from Kaiyang Freeway, China. The results of Bayesian estimation conducted in WinBUGS show that significant spatial autocorrelation and spillover effects simultaneously exist in the freeway crash-frequency data. The lower value of deviance information criterion (DIC) and more significant exogenous variables for the hybrid model compared to the other alternatives, indicate the strength of accounting for both spatial autocorrelation and spillover effects on improving model fit and identifying crash contributing factors. Moreover, the model results highlight the importance of daily vehicle kilometers traveled, and horizontal and vertical alignments of targeted segments and adjacent segments on freeway crash occurrences.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

Wen

Zhang

Zeng

et al. 2019

IJERPH

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“…Unobserved heterogeneity and spatial correlation were addressed, and the results helped to prioritize area-wide safety initiatives and programs. Besides bivariate regression models, different multivariate regression models, for example, multivariate tobit analysis [29][30][31][32], Bayesian multivariate approach [33,34], multivariate spatial or/and temporal models [35][36][37][38][39], and mixture of abovementioned models, have been presented to address correlation and unobserved heterogeneity among injury severities.…”

Section: Introductionmentioning

confidence: 99%

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Xiao

Yuan

Kang

et al. 2021

Discrete Dynamics in Nature and Society

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This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

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“…The curvature has been found positively correlated with accident occurrence [24,37,54]. Wen et al [56] found that the curvature was positively associated with the property damage accidents only. Haq et al [31] found a decreasing impact of horizontal curve radius on the truck related accidents.…”

Section: Geometric Design Elementsmentioning

confidence: 95%

“…Many researchers [17,18,40,41,43,46,55] found the expressway segment length to be positively correlated with number of accidents. Wen et al [56] used variable daily vehicle kilometer travel (DVKT) and found it positively associated with accident frequencies. Similarly, Wen et al [11] used variable monthly vehicle kilometer travel (MVKT) and found positively correlated with the accident frequency.…”

Section: Exposurevariablesmentioning

confidence: 99%

Accident prediction modelling for expressways: a review

Kumar

Jain

Singh

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Expressways are the need for fast movement of goods and human being for long distances in current scenario for every country, but at the same time these facilities have the highest severity rate among all the road categories. Various researchers around the world have tried to find out the effect of traffic volume, road geometry and environmental factors on frequency of accident on expressway by using different accident prediction modeling methods during last three decades. The purpose of this review paper is to find out the appropriate modeling method which can be used to predict accident frequency on the expressways in the developing country like India where traffic conditions, vehicular characteristics, and the driver behaviour are very different from the developing countries and to find out the research areas where the findings are inconclusive. Literature review suggests that among various models used so far, correlated random parameter model found to be the most advanced model to simultaneously account for both the heterogeneous effects of explanatory factors across the road segments and the cross correlations among the random parameter estimates. Findings related to variables like percentage of heavy vehicles, vertical gradient and lane width are not conclusive. However, safety effect of variables like speed limit, roadway lighting, pavement type and fog are less studied on expressway. The effect of allowing two wheelers on the expressways safety has not been studied yet, and the accidents related to fatigue or drowsiness also need examination.

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The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Cited by 25 publications

References 35 publications

Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Accident prediction modelling for expressways: a review

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