The HSM predictive model for rural two-lane two-way highways consists of a safety performance function (SPF), crash modification factors (CMFs), and a jurisdiction-specific calibration factor. For this research, two sample periods were used: a three-year period from 2010 to 2012 and a five-year period from 2008 to 2012. The calibration factor for the HSM predictive model was determined to be 1.50 for the three-year period and 1.60 for the five-year period. These factors are to be used in conjunction with the HSM SPF and all applicable CMFs.

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.

"According to Federal Motor Carrier Safety Administration (FMCSA), truck involvement in fatal crashes is more on rural areas than on urban areas. The Fatality Analysis Reporting System (FARS) encyclopedia also indicates that truck involvement in fatal crashes are approximately 12% of the total fatal crashes in the nation and 14 % in The State of Ohio. One area for potential concern is the role of vertical curves on truck crashes. In the design of vertical curves stopping distance, grade and length of the curve are important factors taken into consideration. Vehicle operations on vertical curves are influenced by the grade of the curve, stopping sight distance and vehicle speed. These factors may create operational issues for vehicles traveling on vertical curves and in turn increase the likelihood for crashes. Truck specific studies in the past have focused on geometric roadway factors associated with crashes on vertical curves. Most of the research studies are focused on crest curve truck crashes, and little research has been done on crashes on vertical sag curves. The main research goal of the study is to develop prediction models to evaluate the impact of geometry, traffic volumes and speed on truck-related crashes on two-lane rural vertical curves. The accomplishment of the research goal is achieved by setting five objectives. The first objective is to develop three crash prediction models using negative binomial regression model. These models are 1. Full model - for all vertical curves 2. Reduced model I - for crest curves only and 3. Reduced model II - for sag curves only. The dataset includes 1,935 vertical curve segments with 205 truck crashes from 2002-2006. In second and third objective, Full Bayes approach is used to enhance the results obtained in the Reduced Models I and II. These results are then compared to the initial models. The fourth objective is evaluating the vertical curve variables which are statistically significant with truck-related crashes. These results show that higher grade change for the length of the vertical curve, total width in the range of 24 to 26ft, more number of passenger cars and trucks, increases the truck-related crashes on both crest and sag curves. Low speed limit on crest curves and high speed limit on sag curves increases truck-related crashes which may seem counter intuitive. The fifth objective is to provide suggestions on effective methods to reduce truck related crashes and improve safety. Some potential areas for design improvement may include flattening of steep vertical curves, advisory speed signs and increasing the roadway width on rural vertical curves in Ohio."--Abstract.

The study area was the state of Utah. Crash data from 2005-2007 on 157 selected study segments provided a 3-year observed crash frequency to obtain a calibration factor for the HSM SPF and develop new SPFs. The calibration factor for the HSM SPF for rural two-lane two-way roads in Utah is 1.16. This indicates that the HSM underpredicts the number of crashes on rural two-lane two-way roads in Utah by sixteen percent.

The study area was the state of Utah. Crash data from 2005-2007 on 157 selected study segments provided a 3-year observed crash frequency to obtain a calibration factor for the HSM SPF and develop new SPFs. The calibration factor for the HSM SPF for rural two-lane two-way roads in Utah is 1.16. This indicates that the HSM underpredicts the number of crashes on rural two-lane two-way roads in Utah by sixteen percent.