The covariance between the number of accidents and the number of victims in multivariate analysis of accident related outcomes

Bijleveld, F D

doi:10.1016/j.aap.2005.01.004

Cited by 56 publications

(21 citation statements)

References 8 publications

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“…Different approaches can be used for collision severity analysis: (a) incorporating severity into the collision frequency models by modeling collisions classified by severity types [31][32][33][34]; and (b) modeling the conditional probability of each severity level for a given collision [14,15,17,35,36]. In this research, we adopted the second approach for three reasons: (i) different factors could have different effects on collision occurrence and severity (e.g., seat belt use has nothing to do with collision occurrence, but is an important factor in severity analysis); (ii) data that could be used for joint models are limited in nature because most of the data are collected after the collision has happened [12]; and, (iii) consequence outcomes and injury data are at the individual, vehicle, or accident level.…”

Section: Model Developmentmentioning

confidence: 99%

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Usman

Miranda-Moreno

2016

J. Mod. Transport.

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This paper describes an empirical study aiming at identifying the main differences between different logistic regression models and collision data aggregation methods that are commonly applied in road safety literature for modeling collision severity. In particular, the research compares three popular multilevel logistic models (i.e., sequential binary logit models, ordered logit models, and multinomial logit models) as well as three data aggregation methods (i.e., occupant based, vehicle based, and collision based). Six years of collision data (2001)(2002)(2003)(2004)(2005)(2006) from 31 highway routes from across the province of Ontario, Canada were used for this analysis. It was found that a multilevel multinomial logit model has the best fit to the data than the other two models while the results obtained from occupant-based data are more reliable than those from vehicle-and collision-based data. More importantly, while generally consistent in terms of factors that were found to be significant between different models and data aggregation methods, the effect size of each factor differ substantially, which could have significant implications for evaluating the effects of different safety-related policies and countermeasures.

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Section: Model Developmentmentioning

confidence: 99%

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Usman

Miranda-Moreno

2016

J. Mod. Transport.

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“…Recognizing the need for such considerations, Ladron de Guevara and Washington (2004) investigated the simultaneity of fatality and injury crash outcomes. Bijleveld (2005) also examined the correlation structure between crash and injury counts. As expected, he found significant correlations.…”

Section: Introductionmentioning

confidence: 99%

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

Kockelman²,

Damien

2008

Accident Analysis & Prevention

328

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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“…Accident risk is considered based on three major dimensions such as the exposure, the accident and injury occurrence probabilities and the corresponding consequences. The most common approach applied in early works is to model the interaction between road geometry, traffic In such studies, univariate counting models for only one single model response variable are used, implying, for example that the number of accidents corresponding to different degrees of injury severity are modelled separately without taking into account the dependencies that exist between them [21,22]. Such dependencies are considered in more recent studies where the different response variables are modelled jointly using multivariate modelling techniques [23][24][25].…”

Section: Prediction Of Road Accidents Based On Bayesian Methodsmentioning

confidence: 99%

Different Methods of Accident Forecast Based on Real Data

VK¹

2015

J Civil Environ Eng

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The covariance between the number of accidents and the number of victims in multivariate analysis of accident related outcomes

Cited by 56 publications

References 8 publications

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

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

Different Methods of Accident Forecast Based on Real Data

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