Spatial analysis of alcohol-related motor vehicle crash injuries in southeastern Michigan

Meliker, Jaymie R.; Maio, Ronald F.; Zimmerman, Marc A.; Kim, Hyungjin Myra; Smith, Sarah C.; Wilson, Mark L.

doi:10.1016/j.aap.2004.06.002

Cited by 29 publications

(17 citation statements)

References 29 publications

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“…The assumption of no spatial correlation is actually too strong in this case. These uncontrolled (or simply unobserved) factors may also render significant spatial correlations over time (see, e.g., Meliker et al, 2004;Pawlovich et al, 1998.) Additionally, the high level of correlation between PDO and disabling crashes may indicate some ambiguity or weakness in severity classification schemes, if one believes that unobserved heterogeneity in omitted variables should generate significant correlation (e.g., in data sets with relatively few control variables available).…”

Section: Discussionmentioning

confidence: 99%

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Kockelman²,

Damien

2008

Accident Analysis & Prevention

329

134

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The following paper is a pre-print and the final publication can be found in Accident Analysis and Prevention, 40 (3):964-975, 2008. Presented at the 86 th Annual Meeting of the Transportation Research Board, January 2007 ABSTRACT Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models injuries by severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses some questions that are difficult to answer by estimating them separately. With recent advancements in crash modeling and Bayesian statistics, the parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways.The estimation results from the MVPLN approach did show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggested overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves. KEY WORDS INTRODUCTIONRoadway safety is a major concern for the general public and public agencies. Roadway crashes claim many lives and cause substantial economic losses each year. In the U.S. traffic crashes bring about more loss of human life (as measured in human-years) than almost any other causefalling behind only cancer and heart disease (NHTSA, 2005). The situation is of particular interest on rural two-lane roadways, which experience significantly higher fatality rates than urban roads. The annual cost of traffic crashes is estimated to be $231 billion, or $820 per capita in 2000(Blincoe et al., 2000. These costs do not include the cost of delays imposed on other travelers, which also are significant, particularly when crashes occur on busy roadways. Schrank and Lomax (2002) estimate that over half of all traffic delays are due to non-recurring events, such as crashes, costing on the order of $1,000 per peak-period driver per year, particularly in urban areas. Thus, while vehicle and roadway design are improving, and growing congestion may be reducing impact speeds, crashes are becoming more critical in many ways, particularly in societies that continue to motorize. Given the importance of roadways safety, there has been considerable crash prediction research (see, e.g., Hauer, 1986Hauer, , 1997Hauer,...

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Section: Discussionmentioning

confidence: 99%

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Kockelman²,

Damien

2008

Accident Analysis & Prevention

329

134

View full text Add to dashboard Cite

show abstract

“…For example, over 2 million deaths have been reported worldwide [1] , and every year 1.5 million people are injured from vehicle accidents in the USA [2] . The injuries from traffic accidents cause serious social and economic problems, and are a threat to public health [3] . The health problems related to traffic accidents are not only physical injuries, but also comprise psychological problems, particularly posttraumatic stress disorder (PTSD).…”

Section: Introductionmentioning

confidence: 99%

Predictive Factors for Acute Stress Disorder and Posttraumatic Stress Disorder after Motor Vehicle Accidents

Yaşan

Güzel²,

Tamam³

et al. 2009

Psychopathology

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Background: Since traffic accidents are more common in developing countries than in developed countries, we aimed to investigate the association of several factors with the development and persistence of posttraumatic stress disorder (PTSD) after traffic accidents. Sampling and Methods: In the study,95 participants with injuries from traffic accidents were evaluated at 4 different times: in the beginning, and after 3, 6 and 12 months. Results: During the first evaluation, 41.1% (39) of our participants had acute stress disorder (ASD). It was found that lower perceived social support (OR = 0.0908, 95% CI = 0.834–0.989, p = 0.027) and higher peritraumatic dissociative experience scores (OR = 1.332, 95% CI = 1.170–1.516, p < 0.001) were significant predictors of ASD. In the evaluations after 3, 6 and 12 months after the accident, we found PTSD affected 29.8, 23.1 and 17.9% of the participants, respectively. Although limitations at work and in social life after a traffic accident were not related to PTSD at 3 months (OR = 122.43, 95% CI = 0.000, p = 0.999) or at 6 months (OR = 63.438, 95% CI = 0.529–76.059, p = 0.089), limitations at work and in social life were predictors of PTSD at 12 months (OR = 155.514, 95% CI = 2.321–104.22, p = 0.019). Conclusions: The persistence of PTSD at the 12-month evaluation is related to ASD, limitations in work and social life, and lower social support scores. In developing countries like Turkey, long-term PTSD is commonly seen after traffic accidents.

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“…[4] These injuries appear as an important public health problem not just due to high economic burden, but also to the relevant social problems. [9] Among the one thousand eight hundred and fifteen forensic death cases within the 6-year period covered by the present study, cause of death was determined to be traffic accidents in 334 (18.4%) cases. In other studies conducted in Turkey, this rate has been reported to be 35.9% by Tıraşçı and Gören, [10] 47.8% by Çakıcı et al, [11] 48.7% by Karagöz et al, [12] 31.3% by Gören et al, [13] and 38.8% by Karbeyaz et al [14] A study from Sri Lanka has reported this rate to be 43.5%, [15] while it has been reported to be 57.9% in a study from Nigeria [16] and 18.6% in a study from Norway.…”

Section: Discussionmentioning

confidence: 99%

Features of Traffic Accidents Occurred in Aydin Province between 2005 and 2011

Dirlik¹,

Bostancıoğlu²,

Elbek³

et al. 2014

Ulus Travma Acil Cerrahi Derg

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Spatial analysis of alcohol-related motor vehicle crash injuries in southeastern Michigan

Cited by 29 publications

References 29 publications

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Predictive Factors for Acute Stress Disorder and Posttraumatic Stress Disorder after Motor Vehicle Accidents

Features of Traffic Accidents Occurred in Aydin Province between 2005 and 2011

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