Traffic safety facts 1996 - a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 2000: a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 2001: a compilation of motor vehicle crash data from the Fatality Analysis Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 2003: a compilation of motor vehicle crash data from the Fatality Analysis Reporting System and the General Estimates System.

Both systems were designed and developed by NHTSA's National Center for Statistics and Analysis (NCSA) to provide an overall measure of highway safety, to help identify traffic safety problems, to suggest solutions, and to help provide an objective basis on which to evaluate the effectiveness of motor vehicle safety standards and highway safety initiatives. Data from these systems are used to answer requests for information from the international and national highway traffic safety communities, including state and local governments, the Congress, Federal agencies, research organizations, industry, the media, and private citizens.

Traffic safety facts 1994 - a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 1997 - a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 1998 - a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 1999: a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 2002: a compilation of motor vehicle crash data from the Fatality Analysis Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 1992 - a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 1993 - a compilation of motor vehicle crash data from the Fatal Accident Reporting System and the General Estimates System.

Mode of access: Internet.

Crash testing of experimental safety vehicles. Volume II - Renault Basic Research Vehicle. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Safety Edge Crash Modification Factors, Tech Brief, 2016

This study evaluated the safety impact of the Safety Edge for construction projects in 2010 and 2011 in Iowa to assess the effectiveness of the treatment in reducing crashes.

Systematic Identification of High Crash Locations Final Report, 2001

Federal and state policy makers increasingly emphasize the need to reduce highway crash rates. This emphasis is demonstrated in Iowa’s recently released draft Iowa Strategic Highway Safety Plan and by the U.S. Department of Transportation’s placement of “improved transportation safety” at the top of its list of strategic goals. Thus, finding improved methods to enhance highway safety has become a top priority at highway agencies. The objective of this project is to develop tools and procedures by which Iowa engineers can identify potentially hazardous roadway locations and designs, and to demonstrate the utility of these tools by developing candidate lists of high crash locations in the State. An initial task, building an integrated database to facilitate the tools and procedures, is an important product, in and of itself. Accordingly, the Iowa Department of Transportation (Iowa DOT) Geographic Information Management System (GIMS) and Geographic Information System Accident Analysis and Location System (GIS-ALAS) databases were integrated with available digital imagery. (The GIMS database contains roadway characteristics, e.g., lane width, surface and shoulder type, and traffic volume, for all public roadways. GIS-ALAS records include data, e.g., vehicles, drivers, roadway conditions, and the crash severity, for crashes occurring on public roadways during then past 10 years.)