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Journal ArticleDOI

Use of Structural Equation Modeling to Measure Severity of Single-Vehicle Crashes

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TLDR
In this paper, structural equation modeling (SEM) offers the opportunity to explore the complex relationships between variables by handling endogenous variables and exogenous variables simultaneously, and allows latent variables to be included in the model and bridges the gap between dependent and explanatory variables.
Abstract
Injury severity and vehicle damage are two of the main indicators of the level of crash severity. Other factors, such as driver characteristics, roadway conditions, highway geometry, environmental factors, vehicle type, and roadside objects, may also be directly or indirectly related to crash severity. All these factors interact in such complicated ways that it is often difficult to identify their interrelationships. The aim of this study was to examine the relationships between these contributors and the severity of single-vehicle crashes. Structural equation modeling (SEM) offers the opportunity to explore the complex relationships between variables by handling endogenous variables and exogenous variables simultaneously. Furthermore, SEM allows latent variables to be included in the model and bridges the gap between dependent and explanatory variables. In this study, the number of latent variables was defined by the understanding of collision force, kinetic energy, and mechanical process of a collision,...

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Citations
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Vehicle Occupant Injury Severity on Highways: Empirical Investigation

TL;DR: A random parameters ordered probit model is applied to explore the influence of speed and traffic volume on the injury level sustained by vehicle occupants involved in accidents on the A4-A86 junction in the Paris region and results indicate that increased traffic volume has a consistently positive effect on severity.
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What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors

TL;DR: Results show that while the presence of gates is not directly associated with injury severity, the indirect effect through stopping behavior is statistically significant (95% confidence level) and substantial and can be used to modify Crash Modification Factors (CMFs) to account for crash injury severity.
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Safety sensitivity to roadway characteristics: A comparison across highway classes.

TL;DR: The results suggest that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume and average vertical grade, but less sensitive to the inside shoulder width and the median width, with regard to the highest class (Interstates).
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City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects

TL;DR: Urban traffic is safer in UAs with more uniform job-housing balance among their different tracts, more polycentric design, and less low-density sprawl, and the importance of incorporating city-level land-use policies into the planning practices, in terms of traffic safety is demonstrated.
References
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Book

Principles and Practice of Structural Equation Modeling

TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Journal ArticleDOI

Alternative Ways of Assessing Model Fit

TL;DR: In this paper, two types of error involved in fitting a model are considered, error of approximation and error of fit, where the first involves the fit of the model, and the second involves the model's shape.
Journal ArticleDOI

Correlation and Causation

TL;DR: Causality is the area of statistics that is most commonly misused, and misinterpreted, by nonspecialists as discussed by the authors, who fail to understand that, just because results show a correlation, there is no proof of an underlying causality.
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