The purpose of this study was to examine the contributing factors and characteristics associated with fatality and injuries sustained by drivers of emergency vehicles (EVs) involved in traffic crashes in the state of Ohio. Emergence vehicle drivers considered in this study include drivers of firefighter truck, drivers of ambulances (emergency medical services), and law enforcement officers. Some few research efforts recently conducted using Ohio0́9s crash data have shown that emergency vehicles are significant factors in increasing crash and injury severity levels. The current study investigated the injury risk factors of crashes involving EVs by using Ohio crash data for 2011-2015. A binary logistic regression model was developed to identify statistically significant factors related to fatalities and injuries of EV drivers. The logistic regression model identified fourteen factors. Significant factors identified include type of crash, collision type, speed related, traffic control type, alcohol related, type of emergency vehicle, emergency related trip, female driver, light condition, teen-related, not using seatbelt, curved and grade segment. Educational and enforcement strategies can be used to reduce EV related crashes and injuries.

Reports for 1975- include activities under the National traffic and motor vehicle safety act of 1966 and the Motor vehicle information and cost savings act of 1972.

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.