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.

Industrial development is essential to improvement of the standard of living in all coun tries. In a given region, old and new plants, processes, and technologies have to coexist Technological penetration and substitution processes are generally taking place; they are entirely dynamic and this trend is going to stay like this. People's health and the environment can be affected, directly or indirectly, by rou tine waste discharges or by accidents. A series of recent major industrial accidents and the effect of poUution highlighted, once again, the need for better management of rou tine and accidental risks. Moreover, the existence of natural hazards complicate even more the situation in any given region. Managing the hazards of modern technological systems has become a key activity in highly industrialized countries. Decision makers are often confronted with complex issues concerning economic and social development, industrialization and associated infrastructure needs, population and land use planning. Such issues have to be ad dressed in such a way that ensures that public health wiD not be disrupted or substan tially degraded.

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.

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

Covers empirical approaches to outlier detection in intelligent transportation systems data, modeling of traffic crash-flow relationships for intersections, profiling of high-frequency accident locations by use of association rules, analysis of rollovers and injuries with sport utility vehicles, and automated accident detection at intersections via digital audio signal processing.

TRB's Transportation Research Record: Journal of the Transportation Research Board, No. 2083 includes 22 papers that explore data-driven perspective on safety risk management, macrolevel annual safety performance measures, tool with road-level crash prediction for safety planning, congestion and number of lanes on urban freeways relationship to safety, accident modification factors, identifying hazardous road locations, identifying hot spots, and safety influence area for four-legged signalized intersections. This issue of the TRR also examines automated analysis of accident exposure, new simulation-based surrogate safety measure, hit-and-run crashes, speed limit increases' effect on injury severity, safety of curbs, proximity to intersections and injury severity of urban arterial crashes, nested logit model of traffic flow on freeway ramps, intelligent transportation system data for assessing freeway safety, vehicle time spent in following on two-lane rural roads, indirect associations in crash data, crash prediction models for rural highways, and methodology for identifying causal factors of accident severity.

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.