A joint model of crash type and severity for two-vehicle crashes
01 Jan 2008-
About: The article was published on 2008-01-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Crash.
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01 Jan 1987
TL;DR: In this paper, the authors analyzed over 9000 accidents involving large trucks and combination vehicles during a two-year period on freeways in the greater Los Angeles area and analyzed relative to collision factors, accident severity, incident duration, and lane closures.
Abstract: Data associated with over 9000 accidents involving large trucks and combination vehicles during a two-year period on freeways in the greater Los Angeles area are analyzed relative to collision factors, accident severity, incident duration, and lane closures. Relationships between type of collision and accident characteristics are explored using log-linear models. The results point to significant differenes in several immediate consequences of truck-related freeway accidents according to collision type. These differences are associated both with the severity of the accident, in terms of injuries and fatalities, and the impact of the accident on system performance, in terms of incident duration and lane closures. Hit-objects and broadside collisions are the most severe types in terms of fatalities and injuries, respectively, and single-vehicle accidents are relatively more severe than two-vehicle accidents. The durations of accident incidents are found to be log-normally distributed for homogeneous groups of truck accidents, categorized according to type of collision and, insome instances, severity. The longest durations are typically associated with overturns.
13 citations
Dissertation•
23 Jul 2018
TL;DR: In this article, the authors present a review of the literature in the field of computer science with a focus on the impact of temporary traffic control (TTC) zones in work zones.
Abstract: ................................................................................................................................... ii ACKNOWLEDGEMENTS ........................................................................................................... iii LIST OF TABLES ......................................................................................................................... vi LIST OF FIGURES ..................................................................................................................... viii LIST OF ABBREVIATIONS ........................................................................................................ ix CHAPTER ONE: INTRODUCTION ..............................................................................................1 1.1 Background ............................................................................................................................1 1.2 Temporary Traffic Control (TTC) Zone ................................................................................3 1.3 Crash Database .......................................................................................................................5 1.4 Work Zone Crash Type and Severity .....................................................................................6 1.5 Research Objectives ...............................................................................................................8 1.6 Thesis Outline ........................................................................................................................9 CHAPTER TWO: LITERATURE REVIEW ................................................................................10 2.
2 citations
Cites methods from "A joint model of crash type and sev..."
...…level of severity simultaneously for two-vehicle crashes was done in a previous study using a joint unordered-ordered discrete model in which crash type was treated as an unordered discrete outcome variable and severity level was treated as an ordered discrete response variable (Ye et al., 2008)....
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References
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01 Jan 2003TL;DR: In this paper, the authors proposed a sampling-based approach for estimating Elasticities in time series regression models, which can be used to estimate a single Beta Parameter for m - 1 of the m Levels of a Variable Checking Regression Assumptions Regression Outliers Regression Model GOF Measures Multicollinearity in the Regression Regression model-Building Strategies Estimating Elasticities Censored Dependent Variables-Tobit Model Box-Cox Regression Violations of Regression this paper
Abstract: FUNDAMENTALS Statistical Inference I: Descriptive Statistics Measures of Relative Standing Measures of Central Tendency Measures of Variability Skewness and Kurtosis Measures of Association Properties of Estimators Methods of Displaying Data Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons Confidence Intervals Hypothesis Testing Inferences Regarding a Single Population Comparing Two Populations Nonparametric Methods CONTINUOUS DEPENDENT VARIABLE MODELS Linear Regression Assumptions of the Linear Regression Model Regression Fundamentals Manipulating Variables in Regression Estimate a Single Beta Parameter Estimate Beta Parameter for Ranges of a Variable Estimate a Single Beta Parameter for m - 1 of the m Levels of a Variable Checking Regression Assumptions Regression Outliers Regression Model GOF Measures Multicollinearity in the Regression Regression Model-Building Strategies Estimating Elasticities Censored Dependent Variables-Tobit Model Box-Cox Regression Violations of Regression Assumptions Zero Mean of the Disturbances Assumption Normality of the Disturbances Assumption Uncorrelatedness of Regressors and Disturbances Assumption Homoscedasticity of the Disturbances Assumption No Serial Correlation in the Disturbances Assumption Model Specification Errors Simultaneous-Equation Models Overview of the Simultaneous-Equations Problem Reduced Form and the Identification Problem Simultaneous-Equation Estimation Seemingly Unrelated Equations Applications of Simultaneous Equations to Transportation Data Panel Data Analysis Issues in Panel Data Analysis One-Way Error Component Models Two-Way Error Component Models Variable-Parameter Models Additional Topics and Extensions Background and Exploration in Time Series Exploring a Time Series Basic Concepts: Stationarity and Dependence Time Series in Regression Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions Autoregressive Integrated Moving Average Models The Box-Jenkins Approach Autoregressive Integrated Moving Average Model Extensions Multivariate Models Nonlinear Models Latent Variable Models Principal Components Analysis Factor Analysis Structural Equation Modeling Duration Models Hazard-Based Duration Models Characteristics of Duration Data Nonparametric Models Semiparametric Models Fully Parametric Models Comparisons of Nonparametric, Semiparametric, and Fully Parametric Models Heterogeneity State Dependence Time-Varying Covariates Discrete-Time Hazard Models Competing Risk Models COUNT AND DISCRETE DEPENDENT VARIABLE MODELS Count Data Models Poisson Regression Model Interpretation of Variables in the Poisson Regression Model Poisson Regression Model Goodness-of-Fit Measures Truncated Poisson Regression Model Negative Binomial Regression Model Zero-Inflated Poisson and Negative Binomial Regression Models Random-Effects Count Models Logistic Regression Principles of Logistic Regression The Logistic Regression Model Discrete Outcome Models Models of Discrete Data Binary and Multinomial Probit Models Multinomial Logit Model Discrete Data and Utility Theory Properties and Estimation of MNL Models The Nested Logit Model (Generalized Extreme Value Models) Special Properties of Logit Models Ordered Probability Models Models for Ordered Discrete Data Ordered Probability Models with Random Effects Limitations of Ordered Probability Models Discrete/Continuous Models Overview of the Discrete/Continuous Modeling Problem Econometric Corrections: Instrumental Variables and Expected Value Method Econometric Corrections: Selectivity-Bias Correction Term Discrete/Continuous Model Structures Transportation Application of Discrete/Continuous Model Structures OTHER STATISTICAL METHODS Random-Parameter Models Random-Parameters Multinomial Logit Model (Mixed Logit Model) Random-Parameter Count Models Random-Parameter Duration Models Bayesian Models Bayes' Theorem MCMC Sampling-Based Estimation Flexibility of Bayesian Statistical Models via MCMC Sampling-Based Estimation Convergence and Identifi ability Issues with MCMC Bayesian Models Goodness-of-Fit, Sensitivity Analysis, and Model Selection Criterion using MCMC Bayesian Models Appendix A: Statistical Fundamentals Appendix B: Glossary of Terms Appendix C: Statistical Tables Appendix D: Variable Transformations References Index
1,843 citations
TL;DR: In this article, a quasi-random sequence for the estimation of the mixed multinomial logit model was proposed, which accommodates general patterns of competitiveness as well as heterogeneity across individuals in sensitivity to exogenous variables.
Abstract: This paper proposes the use of a quasi-random sequence for the estimation of the mixed multinomial logit model. The mixed multinomial structure is a flexible discrete choice formulation which accommodates general patterns of competitiveness as well as heterogeneity across individuals in sensitivity to exogenous variables. The estimation of this model has been achieved in the past using the pseudo-random maximum simulated likelihood method that evaluates the multi-dimensional integrals in the log-likelihood function by computing the integrand at a sequence of pseudo-random points and taking the average of the resulting integrand values. We suggest and implement an alternative quasi-random maximum simulated likelihood method which uses cleverly crafted non-random but more uniformly distributed sequences in place of the pseudo-random points in the estimation of the mixed logit model. Numerical experiments, in the context of intercity travel mode choice, indicate that the quasi-random method provides considerably better accuracy with much fewer draws and computational time than does the pseudo-random method. This result has the potential to dramatically influence the use of the mixed logit model in practice; specifically, given the flexibility of the mixed logit model, the use of the quasi-random estimation method should facilitate the application of behaviorally rich structures in discrete choice modeling.
965 citations
TL;DR: This paper demonstrates a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions.
737 citations
TL;DR: The results suggest that pickups and sport utility vehicles are less safe than passenger cars under single-vehicle crash conditions and that males and younger drivers in newer vehicles at lower speeds sustain less severe injuries.
594 citations
TL;DR: The similarities and the differences in the factors that affect injury severity between different locations are illustrated.
528 citations
Additional excerpts
...Abdel-Aty (2003) developed ordered probit models to analyze factors affecting injury severity at roadway sections, signalized intersections and toll plazas....
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