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Author: Yingge Xiong Publisher: ISBN: Category : Traffic accident investigation Languages : en Pages : 107
Book Description
The American Association of State Highway Transportation Officials (AASHTO) has established a goal to halve the national number of highway fatalities by 2027. In order to fulfill the states' portion of the goal, efforts are needed on building sophisticated crash injury data analysis methodologies for reliable safety hazards identification in development of state and local safety programs. On account of the considerable amount of unobserved and omitted on-the-spot information in crash datasets used by agencies, the issue of unobserved heterogeneity in crash data modeling has been identified and has attracted growing attention in recent years. Prior studies on relationships between highway safety elements and crash injury severity outcomes have suggested that effects of contributing factors in different situations may be nonhomogeneous. However, little is understood about the dynamics. This dissertation aims to contribute to the literature by (a) investigating how effects of hazardous factors vary across road segments and over time periods and (b) how they would interact with the effects of other factors on crash injury severity outcomes, with accommodation of unobserved heterogeneity in different levels and without prespecified assumptions on probability distributions. The analysis went beyond the heterogeneous effects formulation and included the model estimation details based on Bayesian inference. This dissertation consists two studies: (1) A particular case of cross-sectional unobserved heterogeneity modeling for a safety intervention program was studied by using Indiana adolescent crash data. A Markov Chain Monte Carlo (MCMC) algorithm was developed for estimation and a permutation sampler was extended for model identification. (2) A general case of time-varying unobserved heterogeneity modeling was carried out based on Indiana rural interstate crash data. Reparameterization and partially marginalized conditional samplers techniques were designed to reduce autocorrelation between consecutive draws and to improve the convergence efficiency of chains in estimation simulation. The implications for implementation of regulation enforcement and highway infrastructure upgrade and maintenance were discussed. The empirical results can provide substantial insights to government agencies that are concerned about strategic programming of safety countermeasures to leverage safety intervention resources. The methodologies set forth herein should be of interest to individuals who are developing analysis tools for crash cause diagnosis in state and local transportation safety programs, and have the potential for valuable new insights into a wide variety of questions in discrete data modeling.
Author: Yingge Xiong Publisher: ISBN: Category : Traffic accident investigation Languages : en Pages : 107
Book Description
The American Association of State Highway Transportation Officials (AASHTO) has established a goal to halve the national number of highway fatalities by 2027. In order to fulfill the states' portion of the goal, efforts are needed on building sophisticated crash injury data analysis methodologies for reliable safety hazards identification in development of state and local safety programs. On account of the considerable amount of unobserved and omitted on-the-spot information in crash datasets used by agencies, the issue of unobserved heterogeneity in crash data modeling has been identified and has attracted growing attention in recent years. Prior studies on relationships between highway safety elements and crash injury severity outcomes have suggested that effects of contributing factors in different situations may be nonhomogeneous. However, little is understood about the dynamics. This dissertation aims to contribute to the literature by (a) investigating how effects of hazardous factors vary across road segments and over time periods and (b) how they would interact with the effects of other factors on crash injury severity outcomes, with accommodation of unobserved heterogeneity in different levels and without prespecified assumptions on probability distributions. The analysis went beyond the heterogeneous effects formulation and included the model estimation details based on Bayesian inference. This dissertation consists two studies: (1) A particular case of cross-sectional unobserved heterogeneity modeling for a safety intervention program was studied by using Indiana adolescent crash data. A Markov Chain Monte Carlo (MCMC) algorithm was developed for estimation and a permutation sampler was extended for model identification. (2) A general case of time-varying unobserved heterogeneity modeling was carried out based on Indiana rural interstate crash data. Reparameterization and partially marginalized conditional samplers techniques were designed to reduce autocorrelation between consecutive draws and to improve the convergence efficiency of chains in estimation simulation. The implications for implementation of regulation enforcement and highway infrastructure upgrade and maintenance were discussed. The empirical results can provide substantial insights to government agencies that are concerned about strategic programming of safety countermeasures to leverage safety intervention resources. The methodologies set forth herein should be of interest to individuals who are developing analysis tools for crash cause diagnosis in state and local transportation safety programs, and have the potential for valuable new insights into a wide variety of questions in discrete data modeling.
Author: Jun Deng (Writer on transportation) Publisher: ISBN: Category : Languages : en Pages : 112
Book Description
Safety at intersections is of significant interest to transportation professionals due to the large number of possible conflicts that occur at those locations. In particular, rural intersections have been recognized as one of the most hazardous locations on roads. However, most models of crash frequency at rural intersections, and road segments in general, do not differentiate between crash type (such as angle, rear-end or sideswipe) and injury severity (such as fatal injury, non-fatal injury, possible injury or property damage only). Thus, there is a need to be able to identify the differential impacts of intersection-specific and other variables on crash types and severity levels. This thesis builds upon the work of Bhat et al., (2013b) to formulate and apply a novel approach for the joint modeling of crash frequency and combinations of crash type and injury severity. The proposed framework explicitly links a count data model (to model crash frequency) with a discrete choice model (to model combinations of crash type and injury severity), and uses a multinomial probit kernel for the discrete choice model and introduces unobserved heterogeneity in both the crash frequency model and the discrete choice model, while also accommodates excess of zeros. The results show that the type of traffic control and the number of entering roads are the most important determinants of crash counts and crash type/injury severity, and the results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects.
Author: Dominique Lord Publisher: Elsevier ISBN: 0128168196 Category : Law Languages : en Pages : 504
Book Description
Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials Provides examples and case studies for most models and methods Includes learning aids such as online data, examples and solutions to problems
Author: Simon Washington Publisher: CRC Press ISBN: 0429520751 Category : Technology & Engineering Languages : en Pages : 496
Book Description
The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.
Author: James H. Hedlund Publisher: DIANE Publishing ISBN: 1437900135 Category : Transportation Languages : en Pages : 21
Book Description
More than 1.5 million traffic crashes involving deer occur each year in the U.S. These crashes produce $1.1 billion in vehicle damage & 150 fatalities annually. Deer-related crashes are increasing as both deer populations & vehicular travel increase. Many methods have been used in attempts to reduce deer crashes, often with little scientific foundation & limited evaluation. This paper summarizes the methods & reviews the evidence of their effectiveness & the situations in which each may be useful. The only widely accepted method with evidence of effectiveness is well-designed & maintained fencing, with underpasses or overpasses as appropriate. Other methods using advanced technology require substantial additional research & evaluation.
Author: Dejan Dudich Publisher: ISBN: Category : Truck accidents Languages : en Pages : 94
Book Description
While there have been several efforts to understand large-truck crashes, the relationship between crash factors, crash severity and collision type is not clearly understood. Past studies have utilized different statistical or econometric models to predict the manner of collision at intersections, yet not much attention has been paid to the factors that lead to injury severity by different types of collisions on state and interstate highways. Studying collision types is crucial when identifying potential safety improvements for state and interstate systems. In this study six collision types are explored they are: angled collisions, fixed object collisions, rear end collision both vehicles moving forward, rear end collisions on moving vehicle, sideswipe collision same direction and sideswipe collisions different directions. With these in mind, the aim of this research is to perform exploratory analyses of large truck-involved crashes through the use of advanced econometric techniques that can shed insights on the factors influencing crashes by collision type. Namely, this research utilizes the mixed multinomial logit model to uncover the effects of unobservable factors (unobserved heterogeneity) across crash observations underlying the data generating process. The results of this thesis indicate that complex interactions of various human, vehicle, and road-environment factors due in fact contribute and that some of the model variables varied across observations, validating the choice of the mixed multinomial logit model and separation of data by collision type.
Author: Byung Jung Park Publisher: ISBN: Category : Languages : en Pages :
Book Description
Developing sound or reliable statistical models for analyzing vehicle crashes is very important in highway safety studies. A difficulty arises when crash data exhibit overdispersion. Over-dispersion caused by unobserved heterogeneity is a serious problem and has been addressed in a variety ways within the negative binomial (NB) modeling framework. However, the true factors that affect heterogeneity are often unknown to researchers, and failure to accommodate such heterogeneity in the model can undermine the validity of the empirical results. Given the limitations of the NB regression model for addressing over-dispersion of crash data due to heterogeneity, this research examined an alternative model formulation that could be used for capturing heterogeneity through the use of finite mixture regression models. A Finite mixture of Poisson or NB regression models is especially useful when the count data were generated from a heterogeneous population. To evaluate these models, Poisson and NB mixture models were estimated using both simulated and empirical crash datasets, and the results were compared to those from a single NB regression model. For model parameter estimation, a Bayesian approach was adopted, since it provides much richer inference than the maximum likelihood approach. Using simulated datasets, it was shown that the single NB model is biased if the underlying cause of heterogeneity is due to the existence of multiple counting processes. The implications could be poor prediction performance and poor interpretation. Using two empirical datasets, the results demonstrated that a two-component finite mixture of NB regression models (FMNB-2) was quite enough to characterize the uncertainty about the crash occurrence, and it provided more opportunities for interpretation of the dataset which are not available from the standard NB model. Based on the models from the empirical dataset (i.e., FMNB-2 and NB models), their relative performances were also examined in terms of hotspot identification and accident modification factors. Finally, using a simulation study, bias properties of the posterior summary statistics for dispersion parameters in FMNB-2 model were characterized, and the guidelines on the choice of priors and the summary statistics to use were presented for different sample sizes and sample-mean values.
Author: Jason Christian Anderson Publisher: ISBN: Category : Tobits Languages : en Pages : 101
Book Description
This thesis provides a comparison of advanced econometric frameworks to account for unobserved factors in crash reported data (also referred to as unobserved heterogeneity) while identifying contributing factors by roadway classification for heavy vehicle injury severity and crash rates. The presented thesis provides two manuscripts that expand the literature regarding these advanced econometric methods using Idaho heavy vehicle crash reported data as a case study. The first manuscript utilizes two advanced analytical techniques, namely the random-parameter multinomial logit (also referred to as the mixed logit) and latent class logit, to identify injury severity contributing factors while exploring the empirical results of the two methods. Recent efforts suggest that more studies examining the results of the two approaches be completed to facilitate the identification of a superior framework that can used for future analyses. In comparing overall model fit (log-likelihood values), marginal effects and actual severities versus predicted severities, it was found that the latent class framework for heavy vehicle injury severity analysis performed better for the Idaho crash data. Further, through a model separation test, it was found that road classifications need to be analyzed separately with 99.99% confidence. In regard to the second manuscript, two additional advanced econometric approaches were utilized to investigate the factors that contribute to the number of crashes per million-vehicle-miles-traveled. Again, analysis was completed by road classification, as it was discovered in manuscript one that road classifications need to be analyzed separately. Due to the skewed distribution of heavy vehicle crash rates, Tobit regression was applied and compared to the empirical results of a latent class Tobit regression framework. To determine the most statistically significant method, overall model fit, partial effects and actual crash rates versus predicted crash rates were evaluated. The latent class Tobit regression framework outperformed that of the traditional Tobit regression approach for the Idaho dataset. Through the comparison of the crash analysis framework, latent class logit and latent class Tobit regression were found to outperform their traditional counterparts. In the midst of evaluating the empirical results, this thesis has statistically determined that road classifications need to be analyzed individually. The current thesis extends the literature in regard to heavy vehicle injury severity analysis and fills the noticeable gap that exists for heavy vehicle crash rate analysis. An analytical foundation has been provided and can be used for future studies that need to model discrete outcomes or continuous response variables. Although agencies typically do not use such advanced methods, the results from this thesis can help the Idaho Department of Transportation facilitate crash countermeasures with more precision and allow them to prioritize accordingly.
Author: Yingge Xiong
Author: Yingge Xiong
Author: Jun Deng (Writer on transportation)
Author: William Lee Carlson
Author: Dominique Lord
Author: Simon Washington
Author: 
Author: James H. Hedlund
Author: Dejan Dudich
Author: Byung Jung Park
Author: Jason Christian Anderson