Advanced Econometric Approaches to Modeling Driver Injury Severity PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Advanced Econometric Approaches to Modeling Driver Injury Severity PDF full book. Access full book title Advanced Econometric Approaches to Modeling Driver Injury Severity by Shamsunnahar Yasmin. Download full books in PDF and EPUB format.
Author: Shamsunnahar Yasmin Publisher: ISBN: Category : Languages : en Pages :
Book Description
"The objective of the dissertation is to develop advanced econometric frameworks to address methodological gaps in safety literature while employing these models developed to study important empirical issues. Crash severity analysis has evolved on examining the influence of several factors, comprising of driver characteristics, vehicle characteristics, roadway attributes, environmental factors and crash characteristics on traffic crash related severities. These associated risk factors are critical to assist decision makers, transportation officials, insurance companies, and vehicle manufacturers to make informed decisions to improve road safety, thereby providing empirical evidence regarding the critical factors would allow us to suggest remedial measures to reduce the negative consequences of crash outcomes. To that extent, the current dissertation contributes to the severity analysis with a specific focus on driver injury severity analysis. Road safety researchers have employed several statistical formulations for analyzing the relationship between injury severity and crash related factors. However, there are still several methodological and empirical gaps in safety literature. The specific emphasis of the current dissertation is to contribute substantially towards methodological gaps in the state of the art for driver injury severity analysis along six directions: (1) appropriate model framework, (2) underreporting issue in severity analysis, (3) exogenous factor homogeneity assumption (4) multiple dependent variables in severity analysis, (5) continuum of fatal crashes and (6) data pooling from multiple data sources. In the dissertation, several econometric models are formulated, estimated and validated to address the aforementioned methodological issues through five different empirical studies. The econometric models developed in the dissertation are estimated using police reported crash databases at the regional and the national level from different industrialized countries. Specifically, the dissertation research is undertaken employing General Estimates System and Fatality Analysis Reporting System of the United States and the Victoria crash database of Australia. In addition to making the aforementioned methodological contributions, the dissertation also makes a substantial empirical contribution to the existing safety literature. Specifically, several policy measures in terms of engineering, enforcement, education and emergency response strategies are identified to improve safety situation and to reduce road crash related fatalities." --
Author: Shamsunnahar Yasmin Publisher: ISBN: Category : Languages : en Pages :
Book Description
"The objective of the dissertation is to develop advanced econometric frameworks to address methodological gaps in safety literature while employing these models developed to study important empirical issues. Crash severity analysis has evolved on examining the influence of several factors, comprising of driver characteristics, vehicle characteristics, roadway attributes, environmental factors and crash characteristics on traffic crash related severities. These associated risk factors are critical to assist decision makers, transportation officials, insurance companies, and vehicle manufacturers to make informed decisions to improve road safety, thereby providing empirical evidence regarding the critical factors would allow us to suggest remedial measures to reduce the negative consequences of crash outcomes. To that extent, the current dissertation contributes to the severity analysis with a specific focus on driver injury severity analysis. Road safety researchers have employed several statistical formulations for analyzing the relationship between injury severity and crash related factors. However, there are still several methodological and empirical gaps in safety literature. The specific emphasis of the current dissertation is to contribute substantially towards methodological gaps in the state of the art for driver injury severity analysis along six directions: (1) appropriate model framework, (2) underreporting issue in severity analysis, (3) exogenous factor homogeneity assumption (4) multiple dependent variables in severity analysis, (5) continuum of fatal crashes and (6) data pooling from multiple data sources. In the dissertation, several econometric models are formulated, estimated and validated to address the aforementioned methodological issues through five different empirical studies. The econometric models developed in the dissertation are estimated using police reported crash databases at the regional and the national level from different industrialized countries. Specifically, the dissertation research is undertaken employing General Estimates System and Fatality Analysis Reporting System of the United States and the Victoria crash database of Australia. In addition to making the aforementioned methodological contributions, the dissertation also makes a substantial empirical contribution to the existing safety literature. Specifically, several policy measures in terms of engineering, enforcement, education and emergency response strategies are identified to improve safety situation and to reduce road crash related fatalities." --
Author: Ahmed Kabli Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Crash severity models are typically developed using police reported injury severity databases. However, several research studies have identified various challenges associated with police reported data. Therefore, the current dissertation is focusing on developing high resolution crash severity models based on medical professional driver injury severity reported using Abbreviated Injury Scale for eight body regions. The dissertation focused on developing a disaggregate injury severity modeling framework that can enhance the estimation accuracy of independent variable impacts on severity. Within this broad research vision, the dissertation has multiple objectives. First, a joint random parameters multivariate model structure with as many dimensions as severity by body location was developed. The empirical analysis involves the estimation of Random Parameters Multivariate Generalized Ordered Probit Model that allows for the influence of common unobserved factors affecting the vehicle occupant severity across body locations. Second, we incorporate the influence of temporal factors (observed and unobserved) within a multivariate model system for medical professional generated body region specific injury severity score. For this purpose, we adopt a hybrid econometric modeling approach that accommodates for the unobserved factors. Third, the dissertation compares the predictive performance of the state-of-the-art econometric model with the predictive performance of state-of-the-art machine learning methods. We consider machine learning approaches such as Random Forest, Logistic Regression, Boosting, and Support Vector Machine. Finally, the dissertation applied two approaches. First, analyze dependent variables with an ordered logit framework using the six injury severity levels. The second approach is adopting a hurdle ordered logit framework by splitting the dependent variable into two stages: binary and truncated which exclude the zero cases. The model performance of these approaches are compared using the data of two body regions.
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: Mohamed Osman Publisher: ISBN: Category : Languages : en Pages :
Book Description
Work zones are critical parts of the transportation infrastructure renewal process consisting of rehabilitation of roadways, maintenance, and utility work. Given the specific nature of a work zone (complex arrangements of traffic control devices and signs, narrow lanes, duration) a number of crashes occur with varying severities involving different vehicle sizes. This dissertation proposes a comprehensive discrete choice analysis of injury severity of crashes in work zones on both the crash and occupant levels, in roadway work zones through a comprehensive set of discrete choice econometric frameworks. Robust discrete choice modeling structures are introduced and applied in the field of work zone safety. This dissertation contains three (3) studies representing the empirical analysis conducted to address the following research questions:1. What factors may contribute to the injury severity levels of large-truck crashes in work zones? And what are the robust analytical methods to recognize such factors?2. How do specific work zone configurations affect factors contributing to the levels of injury severity of work zone crashes?3. How does the specific work zone-component-area where a crash has occurred affect factors contributing to the injury severity levels of work zone crashes?The first study investigates the causal factors contributing to injury severity of large truck crashes in work zones. The second study investigates the causal factors contributing to the injury severity of passenger-car occupants for crashes occurring in different work zone configurations (lane closure, lane shift/crossover, shoulder/median, intermittent, and other). The third study investigates the causal factors contributing to driver & rsquo;s injury severity in the different work zone component-areas (advance-warning, transition, activity, and termination areas). The first study compares a comprehensive set of discrete choice modeling structures; Multinomial Logit (MNL) model, Nested Logit (NL) model, Ordered Logit (ORL) model and Generalized Ordered Response Logit (GORL) model. The second and third studies developed the Mixed Generalized Ordered Response Probit (MGORP) modeling framework to conduct the proposed analysis to answer the second and third research questions. The empirical analysis was conducted using work zone crash database in 10 years of the Highway Safety Information System (HSIS).
Author: Azad Salim Abdulhafedh Publisher: ISBN: Category : Languages : en Pages : 243
Book Description
Modeling crash severity is an important component of reasoning about the issues that may affect highway safety. A better understanding of the factors underlying crash severity can be used to reduce the degree of crash severity injury, locate road hazardous sites, and adopt suitable countermeasures. In order to provide insights on the mechanism and behavior of the crash severity injury, a variety of statistical approaches have been utilized to model the relationship between crash severity and potential risk factors. Many of the traditional approaches for analyzing crash severity are limited in that they are based on the assumption that all observations are independent of each other. However, given the reality of vehicle movement in networked systems, the assumption of independence of crash incidence is not likely valid. For instance, spatial and temporal autocorrelations are important sources of dependency among observations that may bias estimates if not considered in the modeling process. Moreover, there are other aspects of vehicular travel that may influence crash severity that have not been explored in traditional analysis approaches. One such aspect is the roadway visibility that is available to a driver at a given time that can impact their ability to react to changing traffic conditions, a characteristics known as sight distance. Accounting for characteristics such as sight distance in crash severity modeling involve moving beyond statistical analysis and modeling the complex geospatial relationships between the driver and the surrounding landscape. To address these limitations of traditional approaches to crash severity modeling, this dissertation first details a framework for detecting temporal and spatial autocorrelation in crash data. An approach for evaluating the sight distance available to drivers along roadways is then proposed. Finally, a crash severity model is developed based upon a multinomial logistic regression approach that incorporates the available sight distance and spatial autocorrelation as potential risk factors, in addition to a wide range of other factors related to road geometry, traffic volume, driver's behavior, environment, and vehicles. To demonstrate the characteristics of the proposed model, an analysis of vehicular crashes (years 2013-2015) along the I-70 corridor in the state of Missouri (MO) and on roadways in Boone County MO is conducted. To assess existing stopping sight distance and decision sight distance on multilane highways, a geographic information system (GIS)-based viewshed analysis is developed to identify the locations that do not conform to AASHTO (2011) criteria regarding stopping and decision sight distances, which could then be used as potential risk factors in crash prediction. Moreover, this method provides a new technique for estimating passing sight distance along two-lane highways, and locating the passing zones and no-passing zones. In order to detect the existence of temporal autocorrelation and whether it's significant in crash data, this dissertation employs the Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test, and the Ljung-Box Q (LBQ) test, and then describes the removal of any significant amount of temporal autocorrelation from crash data using the differencing procedure, and the Cochrane-Orcutt method. To assess whether vehicle crashes are spatially clustered, dispersed, or random, the Moran's I and Getis-Ord Gi* statistics are used as measures of spatial autocorrelation among vehicle incidents. To incorporate spatial autocorrelation in crash severity modeling, the use of the Gi* statistic as a potential risk factor is also explored. The results provide firm evidence on the importance of accounting for spatial and temporal autocorrelation, and sight distance in modeling traffic crash data.
Author: Irfan Uddin Ahmed Publisher: ISBN: Category : Automobile driving in bad weather Languages : en Pages : 222
Book Description
The primary objective of practitioners working on traffic safety is to reduce the number and severity of crashes. The Highway Safety Manual (HSM) provides practitioners with analytical tools and techniques to estimate the expected crash frequency and severity with the aim to identify and evaluate safety countermeasures. Expected crash frequency can be estimated using Safety Performance Functions (SPFs) provided in Part C of the HSM. The HSM provides simple SPFs which are developed using the most frequently used crash counts model, the negative binomial regression model. The rural nature of Wyoming highways coupled with the mountainous terrain (i.e., challenging roadway geometry) make the HSM basic SPFs unsuitable to determine crash contributing factors for Wyoming conditions. In this regard, the objective of this study is to implement advanced statistical methods such as the different functional forms of Negative Binomial, and Bayesian approach, to develop crash prediction models, investigate crash contributing factors, and determine the impact of safety countermeasures. Bayesian statistics in combination with the power of Markov Chain Monte Carlo (MCMC) sampling techniques provide frameworks to model small sample datasets and complex models at the same time, where the traditional Maximum Likelihood Estimation (MLE) based methods tend to fail. As such, a novel No-U-Turn Sampler for Hamiltonian Monte Carlo (NUTS HMC) sampling technique in a Bayesian framework was utilized to investigate the crash frequency, injury severity of crashes on the interstate freeways and some rural highways in Wyoming. The Poisson and the Negative Binomial (NB) models are the most commonly used regression models in traffic safety analysis. The advantage of the NB model can be further enhanced by providing different functional forms of the variance and the dispersion structure. The NB-2 is the most common form of the NB model, typically used in developing safety performance functions (SPFs) largely due to the mean-variance quadratic relationship. However, studies in the literature have shown that the mean-variance relationship could be unrestrained. Another introduced formulation of the NB model is NB-1, which assumes that there is a constant ratio linking the mean and the variance of the crash frequencies. A more general type of the NB model is the NB-P model, which does not constrain the mean-variance relationship. Thus, leveraging the power of this unrestrained mean-variance relationship, more accurate safety models could be developed, and these would lead to more accurate estimation of crash risk and benefits of potential solutions. This study will help practitioners to implement advanced methodologies to solve traffic safety problems of rural highways that have plagued the researchers for a long time now. The methodologies proposed in this study will help practitioners to replace the outdated and inefficient traditional models and obtain more accurate traffic safety models to predict crashes and the resulting crash injury severity. Moreover, this research quantified the safety effectiveness of some unique countermeasures on rural highways.
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: Publisher: ISBN: Category : Automobiles Languages : en Pages : 215
Book Description
Motor vehicle crashes are a leading cause of death in the United States, cost Americans $277 billion annually, and generate serious psychological burdens. As a result, extensive vehicle safety research focusing on the explanatory factors of crash severity is undertaken using a wide array of methodological techniques including traditional statistical models and contemporary data mining approaches. This study advances the methodological frontier of crash severity research by completing an empirical investigation that compares the performance of popular, longstanding techniques of multinomial logit and ordinal probit models with more recent methods of decision tree and artificial neural network models. To further the investigation of the benefits of data analytics, individual models are combined into model ensembles using three popular combinatory techniques. The models are estimated using 2002 to 2012 crash data from the Missouri State Highway Patrol Traffic Division - Statewide Traffic Accident Records System database, and variables examined include various driver characteristics, temporal factors, weather conditions, road characteristics, crash type, crash location, and injury severity levels. The accuracy and discriminatory power of explaining crash severity outcomes among all methods are compared using classification tables, lift charts, ROC curves, and AUC values. The CHAID decision tree model is found to have the greatest accuracy and discriminatory power relative to all evaluated modeling approaches. The modeling reveals that the presence of alcohol, driving at speeds that exceed the limit, failing to yield, driving on the wrong side of the road, violating a stop sign or signal, and driving while physically impaired lead to a large number of fatalities each year. Yet, the effect of these factors on the probability of a severe outcome is dependent upon other variables, including number of occupants involved in the crash, speed limit, lighting condition, and age of the driver. The CHAID decision tree is used in conjunction with prior literature and the current Missouri rules of the road to provide better formulated driving policies. This study concludes that policy makers should consider the interaction of conditions and driver related contributing factors when crafting future legislation or proposing modifications in driving statues.
Author: Shamsunnahar Yasmin
Author: Shamsunnahar Yasmin
Author: Ahmed Kabli
Author: Jason Christian Anderson
Author: William Lee Carlson
Author: Ahmad Khorashadi
Author: Mohamed Osman
Author: Azad Salim Abdulhafedh
Author: Irfan Uddin Ahmed
Author: Jun Deng (Writer on transportation)
Author: