Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis

Hosseinpour, Mehdi; Sahebi, Sina; Zamzuri, Zamira Hasanah; Yahaya, Ahmad Shukri; Ismail, Noriszura

doi:10.1016/j.aap.2018.05.003

Cited by 40 publications

(22 citation statements)

References 87 publications

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“…Compared to unknown crash types, the injury severity is reduced with the changing from angle to noncollision, which is understandable. Among all the crash types, angle and rear-end crashes frequently occur, accounting for about 85% and leading to different injury severities as verified by Xu et al [44] and Hosseinpour et al [45]. With the crash type from angle to noncollision, the injury severity of novice drivers is reduced about 110%.…”

Section: Resultsmentioning

confidence: 94%

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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Section: Resultsmentioning

confidence: 94%

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

View full text Add to dashboard Cite

show abstract

“…The random errors are assumed to be distributed according to a type I extreme value distribution. Predictions based on MMNL regression can therefore be expected to be more accurate than the results from the standard MNL model because it is a more flexible and powerful approach relaxing the limitations of a MNL model [17,37–41].…”

Section: Literature Reviewmentioning

confidence: 99%

Crash severity analysis of nighttime and daytime highway work zone crashes

Zhang

Hassan

2019

PLoS ONE

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Introduction Egypt’s National Road Project is a large infrastructure project which presently aims to upgrade 2500 kilometers of road networks as well as construct 4000 kilometers of new roads to meet today’s need. This leads to an increase in the number of work zones on highways and therefore a rise in hazardous traffic conditions. This is why highways agencies are shifting towards night construction in order to reduce the adverse traffic impacts on the public. Although many studies have investigated work zone crashes, only a few studies provide comparative analysis of the difference between nighttime and daytime work zone crashes. Methods Data from Egyptian long-term highway work zone projects between 2010 and 2016 are studied with respect to the difference in injury severity between nighttime and daytime crashes by using separate mixed logit models. Results The results indicate that significant differences exist between factors contributing to injury severity. Four variables are found significant only in the nighttime model and four other variables significant in the daytime model. The results show that older and male drivers, the number of lane closures, sidewise crashes, and rainy weather have opposite effects on injury severity in nighttime and daytime crashes. The findings presented in this paper could serve as an aid for transportation agencies in development of efficient measures to improve safety in work zones.

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“…So far, numerous traffic safety analysis models have been developed. Since the frequency of traffic accident is nonnegative and integer, many studies assumed such events follow a Poisson distribution and modelled the frequency of traffic accidents using a Poisson regression model [5,6]. However, the Poisson model cannot handle overdispersed or underdispersed data and may result in biased estimation.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Zhang

Zou

et al. 2020

Journal of Advanced Transportation

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Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

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Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis

Cited by 40 publications

References 87 publications

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

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

Crash severity analysis of nighttime and daytime highway work zone crashes

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

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