This report presents an algorithm for predicting the safety performance of a rural two-lane highway. The accident prediction algorithm consists of base models and accident modification factors for both roadway segments and at-grade intersections on rural two-lane highways. The base models provide an estimate of the safety performance of a roadway or intersection for a set of assumed nominal or base conditions. The accident modification factors adjust the base model predictions to account for the effects on safety for roadway segments of lane width, shoulder width, shoulder type, horizontal curves, grades, driveway density, two-way left-turn lanes, passing lanes, roadside design and the effects on safety for at-grade intersections of skew angle, traffic control, exclusive left- and right-turn lanes, sight distance, and driveways. The accident prediction algorithm is intended for application by highway agencies to estimate the safety performance of an existing or proposed roadway. The algorithm can be used to compare the anticipated safety performance of two or more geometric alternatives for a proposed highway improvement. The accident prediction algorithm includes a calibration procedure that can be used to adapt the predicted results to the safety conditions encountered by any particular highway agency on rural two-lane highways. The algorithm also includes an Empirical Bayes procedure that can be applied to utilize the safety predictions provided by the algorithm together with actual site-specific accident history data.

In recent years, significant effort and money have been invested to enhance highway safety. As available funds decrease, the allocation of resources for safety improvement projects must yield the maximum possible return on investment. Identifying highway locations that have the highest potential for crash reduction with the implementation of effective safety countermeasures is therefore an important first step in achieving the maximum return on safety investment. This study was undertaken to develop safety performance functions (SPFs) for use in Virginia in conjunction with SafetyAnalyst, a computerized analytical tool that can be used for prioritizing safety projects. A safety performance function is a mathematical relationship (model) between frequency of crashes by severity and the most significant causal factors of crashes for a specific type of road. Although the SafetyAnalyst User's Manual recommends four SPFs for two-lane segments, these SPFs were developed using data from Ohio. Because the transferability of these SPFs to other states could not be guaranteed by the developers of the four recommended SPFs, it is necessary to calibrate or develop valid SPFs for each state using appropriate data from the state. In this study, annual average daily traffic (AADT) was used as the most significant causal factor for crashes, emulating the SPFs currently suggested by Safety Analyst. SPFs for two-lane roads in Virginia were developed for total crashes and combined fatal plus injury crashes through generalized linear modeling using a negative binomial distribution for the crashes. Models were developed for urban and rural areas separately, and in order to account for the different topographies in Virginia, SPFs were also separately developed for three regions in Virginia. A total of 139,635 sites were identified for use in this study. Each site is a segment of a rural or urban two-lane road without an intersection for which AADT data were available for the years 2003 through 2007 inclusive and no change in facility type had occurred over that period. A comparative analysis based on the Freeman-Tukey R2 coefficient was then conducted between the relevant Ohio SPFs suggested for use in the SafetyAnalyst User's Manual and those specifically developed in this study for Virginia to determine which set of models better fit the Virginia data. In general, the results indicated that the SPFs specifically developed for Virginia fit the Virginia data better. The final step in this methodology was to illustrate the value of SPFs developed through an analysis of sample sites and the need of the sites for safety improvement based on SPFs as compared to crash rates. The results indicated that prioritization using the empirical Bayes method that incorporates the SPFs resulted in a higher potential for reduction in crashes than did prioritization using crash rates. The effective use of SafetyAnalyst will facilitate the identification of sites with a high potential for safety improvement, which, in turn, with the implementation of appropriate safety improvements, will result in a considerable reduction in crashes and their severity.

This report describes the results of validation and calibration of motor vehicle crash models for rural intersections. Both the validation and recalibration activities were conducted in pursuit of one overriding research objective, which was to make marginal improvements to an existing set of statistical models for predicting crashes at two and four lane intersections, with the primary intent to be used in the Interactive Highway Safety Design Module (IHSDM). The five types of intersection models for which conclusions are drawn and recommendations are made are: Three-legged stop controlled intersections of two-lane roads; four-legged stop controlled intersections of two-lane roads; three-legged stop controlled intersections with two lanes on minor and four lanes on major road; and four-legged stop controlled intersections with two lanes on minor and four lanes on major road, and signalized intersections of two-lane roads.