The Crash Modification Factor (CMF) clearinghouse can be used to estimate benefits for specific highway safety countermeasures. It assists safety professionals in the allocation of investments. The clearinghouse contains over 7000 entries of which only 446 are categorized as intelligent transportation systems or advanced technology, but none directly address connected or autonomous vehicles (CAVs). Further, the effectiveness of highway safety countermeasures is assumed to remain constant over time, an assumption that is particularly problematic as new technologies are introduced. For example, for the existing fleet of human-driven vehicles, installation of rumble strip can potentially reduce “run-off-road” crashes by 40%. If specific CAV technologies, e.g., lane-tracking, can work without rumble strips, and say, half of all cars are so equipped, only half of the fleet will benefit, reducing the benefits of rumble strips by a commensurate amount. Benefits of the two improvements, e.g., rumble strips and automated vehicles, should not be double-counted. As there will still be human-driven and/or non-connected vehicles in the fleet, conventional countermeasures are still necessary, although returns on conventional safety investments may be significantly overestimated. This is important as safety investments should be optimized and geared to future, not past fleets. Moreover, as CMFs are based on historical events, the types of crashes experienced by human-driven, un-connected cars are likely to be much different in the future. This research presents methods to estimate the safety benefits that autonomous vehicles have to offer and the changes needed in CMFs as a result of their adoption. This will primarily be achieved by modifying and enhancing a tool co-developed by the Fellow that estimates the safety benefits of different levels of autonomy. This tool, ddSAFCAT, estimates CAV safety benefits using real-world data for crashes, market penetration, and effectiveness.

This thesis documents the results of the calibration of the Highway Safety Manual (HSM) safety performance function (SPF) for rural two-lane two-way roadway segments in Utah and the development of new SPFs using negative binomial and hierarchical Bayesian modeling techniques. SPFs estimate the safety of a roadway entity, such as a segment or intersection, in terms of number of crashes. The new SPFs were developed for comparison to the calibrated HSM SPF. This research was performed for the Utah Department of Transportation (UDOT).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 new SPFs were developed from the same data that were collected for the HSM calibration, with the addition of new data variables that were hypothesized to have a significant effect on crash frequencies. Negative binomial regression was used to develop four new SPFs, and one additional SPF was developed using hierarchical (or full) Bayesian techniques. The empirical Bayes (EB) method can be applied with each negative binomial SPF because the models include an overdispersion parameter used with the EB method. The hierarchical Bayesian technique is a newer, more mathematically-intense method that accounts for high levels of uncertainty often present in crash modeling. Because the hierarchical Bayesian SPF produces a density function of a predicted crash frequency, a comparison of this density function with an observed crash frequency can help identify segments with significant safety concerns. Each SPF has its own strengths and weaknesses, which include its data requirements and predicting capability. This thesis recommends that UDOT use Equation 5-11 (a new negative binomial SPF) for predicting crashes, because it predicts crashes with reasonable accuracy while requiring much less data than other models. The hierarchical Bayesian process should be used for evaluating observed crash frequencies to identify segments that may benefit from roadway safety improvements.

This thesis contains the results of the development of crash prediction models for curved segments of rural two-lane two-way highways in the state of Utah. The modeling effort included the calibration of the predictive model found in the Highway Safety Manual (HSM) as well as the development of Utah-specific models developed using negative binomial regression. The data for these models came from randomly sampled curved segments in Utah, with crash data coming from years 2008-2012. The total number of randomly sampled curved segments was 1,495. 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. A negative binomial model was used to develop Utah-specific crash prediction models based on both the three-year and five-year sample periods. A backward stepwise regression technique was used to isolate the variables that would significantly affect highway safety. The independent variables used for negative binomial regression included the same set of variables used in the HSM predictive model along with other variables such as speed limit and truck traffic that were considered to have a significant effect on potential crash occurrence. The significant variables at the 95 percent confidence level were found to be average annual daily traffic, segment length, total truck percentage, and curve radius. The main benefit of the Utah-specific crash prediction models is that they provide a reasonable level of accuracy for crash prediction yet only require four variables, thus requiring much less effort in data collection compared to using the HSM predictive model.

This study introduces a multi-objective optimization framework for the re-dimensioning of the cross-section elements of rural horizontal curves with limited sight distance. The optimization aims at minimizing both the risk of collision associated with the limited sight distance and the expected collision frequency corresponding to the cross-section elements’ dimensions. The risk component was assessed using an index known as (Pnc), which is developed based on the reliability theory. The change in collision frequency corresponding to the change of the cross-section elements was extracted from the literature. The risk and the safety components were then combined into one measure (CMFcombined) to develop a direct measure of the safety impacts of the optimization. The proposed framework was applied to five restricted curves in British Columbia, Canada, considering various scenarios. The results showed a considerable reduction in the Pnc value (ranging from 12% to 73%) and the expected collision frequency (ranging from 10% to 31%) after optimization. The estimated combined reduction in collision frequency (CMFcombined) was estimated to vary between 48% and 76%. The results showed that the optimization of cross-section elements can improve the safety of horizontal curves significantly. The framework presented in this study would support transportation engineers in selecting optimal dimensions of cross-section elements of restricted horizontal curves, understanding the safety consequences of selecting a specific cross-section configuration, and assessing the economic viability of different design options. / Thesis / Master of Applied Science (MASc)

Traffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway Traffic Safety Administration (NHTSA, 2013), 33,561 people died in motor vehicle traffic crashes in the United States in 2012, compared to 32,479 in 2011, and it is the first increase in fatalities since 2005. Moreover, in 2012, an estimated 2.36 million people were injured in motor vehicle traffic crashes, compared to 2.22 million in 2011. Due to the demand of highway safety improvements through systematic analysis of specific roadway cross-section elements and treatments, the Highway Safety Manual (HSM) (AASHTO, 2010) was developed by the Transportation Research Board (TRB) to introduce a science-based technical approach for safety analysis. One of the main parts in the HSM, Part D, contains crash modification factors (CMFs) for various treatments on roadway segments and at intersections. A CMF is a factor that can estimate potential changes in crash frequency as a result of implementing a specific treatment (or countermeasure). CMFs in Part D have been developed using high-quality observational before-after studies that account for the regression to the mean threat. Observational before-after studies are the most common methods for evaluating safety effectiveness and calculating CMFs of specific roadway treatments. Moreover, cross-sectional method has commonly been used to derive CMFs since it is easier to collect the data compared to before-after methods. Although various CMFs have been calculated and introduced in the HSM, still there are critical limitations that are required to be investigated. First, the HSM provides various CMFs for single treatments, but not CMFs for multiple treatments to roadway segments. The HSM suggests that CMFs are multiplied to estimate the combined safety effects of single treatments. However, the HSM cautions that the multiplication of the CMFs may over- or under-estimate combined effects of multiple treatments. In this dissertation, several methodologies are proposed to estimate more reliable combined safety effects in both observational before-after studies and the cross-sectional method. Averaging two best combining methods is suggested to use to account for the effects of over- or under- estimation. Moreover, it is recommended to develop adjustment factor and function (i.e. weighting factor and function) to apply to estimate more accurate safety performance in assessing safety effects of multiple treatments. The multivariate adaptive regression splines (MARS) modeling is proposed to avoid the over-estimation problem through consideration of interaction impacts between variables in this dissertation. Second, the variation of CMFs with different roadway characteristics among treated sites over time is ignored because the CMF is a fixed value that represents the overall safety effect of the treatment for all treated sites for specific time periods. Recently, few studies developed crash modification functions (CMFunctions) to overcome this limitation. However, although previous studies assessed the effect of a specific single variable such as AADT on the CMFs, there is a lack of prior studies on the variation in the safety effects of treated sites with different multiple roadway characteristics over time. In this study, adopting various multivariate linear and nonlinear modeling techniques is suggested to develop CMFunctions. Multiple linear regression modeling can be utilized to consider different multiple roadway characteristics. To reflect nonlinearity of predictors, a regression model with nonlinearizing link function needs to be developed. The Bayesian approach can also be adopted due to its strength to avoid the problem of over fitting that occurs when the number of observations is limited and the number of variables is large. Moreover, two data mining techniques (i.e. gradient boosting and MARS) are suggested to use 1) to achieve better performance of CMFunctions with consideration of variable importance, and 2) to reflect both nonlinear trend of predictors and interaction impacts between variables at the same time. Third, the nonlinearity of variables in the cross-sectional method is not discussed in the HSM. Generally, the cross-sectional method is also known as safety performance functions (SPFs) and generalized linear model (GLM) is applied to estimate SPFs. However, the estimated CMFs from GLM cannot account for the nonlinear effect of the treatment since the coefficients in the GLM are assumed to be fixed. In this dissertation, applications of using generalized nonlinear model (GNM) and MARS in the cross-sectional method are proposed. In GNMs, the nonlinear effects of independent variables to crash analysis can be captured by the development of nonlinearizing link function. Moreover, the MARS accommodate nonlinearity of independent variables and interaction effects for complex data structures. In this dissertation, the CMFs and CMFunctions are estimated for various single and combination of treatments for different roadway types (e.g. rural two-lane, rural multi-lane roadways, urban arterials, freeways, etc.) as below: 1) Treatments for mainline of roadway: - adding a thru lane, conversion of 4-lane undivided roadways to 3-lane with two-way left turn lane (TWLTL) 2) Treatments for roadway shoulder: - installing shoulder rumble strips, widening shoulder width, adding bike lanes, changing bike lane width, installing roadside barriers 3) Treatments related to roadside features: - decrease density of driveways, decrease density of roadside poles, increase distance to roadside poles, increase distance to trees Expected contributions of this study are to 1) suggest approaches to estimate more reliable safety effects of multiple treatments, 2) propose methodologies to develop CMFunctions to assess the variation of CMFs with different characteristics among treated sites, and 3) recommend applications of using GNM and MARS to simultaneously consider the interaction impact of more than one variables and nonlinearity of predictors. Finally, potential relevant applications beyond the scope of this research but worth investigation in the future are discussed in this dissertation.

Traffic safety has been considered one of the most important issues in the transportation field. Crashes have caused extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence. With these consistent efforts, both fatalities and fatality rates from road traffic crashes in many countries have been steadily declining over the last ten years. Nevertheless, according to the World Health Organization, the world still lost 1.24 million lives from road traffic crashes in the year of 2013. And without action, traffic crashes on the roads network are predicted to result in deaths of around 1.9 million people, and up to 50 million more people suffer non-fatal injuries annually, with many incurring a disability as a result of their injury by the year 2020. To meet the transportation needs, the use of expressways (toll roads) has risen dramatically in many countries in the past decade. In fact, freeways and expressways are considered an important part of any successful transportation system. These facilities carry the majority of daily trips on the transportation network. Although expressways offer high level of service, and are considered the safest among other types of roads, traditional toll collection systems may have both safety and operational challenges. The traditional toll plazas still experience many crashes, many of which are severe. Therefore, it becomes more important to evaluate the traffic safety impacts of using different tolling systems. The main focus of the research in this dissertation is to provide an up-to-date safety impact of using different toll collection systems, as well as providing safety guidelines for these facilities to promote safety and enhance mobility on expressways. In this study, an extensive data collection was conducted that included one hundred mainline toll plazas located on approximately 750 miles of expressways in Florida. Multiple sources of data available online maintained by Florida Department of Transportation were utilized to identify traffic, geometric and geographic characteristics of the locations as well as investigating and determination of the most complete and accurate data. Different methods of observational before-after and Cross-Sectional techniques were used to evaluate the safety effectiveness of applying different treatments on expressways. The Before-After method includes Naive Before-After, Before-After with Comparison Group, and Before-After with Empirical Bayesian. A set of Safety Performance Functions (SPFs) which predict crash frequency as a function of explanatory variables were developed at the aggregate level using crash data and the corresponding exposure and risk factors. Results of the aggregate traffic safety analysis can be used to identify the hazardous locations (hot spots) such as traditional toll plazas, and also to predict crash frequency for untreated sites in the after period in the Before-After with EB method or derive Crash Modification Factors (CMF) for the treatment using the Cross-Sectional method. This type of analysis is usually used to improve geometric characteristics and mainly focus on discovering the risk factors that are related to the total crash frequency, specific crash type, and/or different crash severity levels. Both simple SPFs (with traffic volume only as an explanatory variable) and full SPFs (with traffic volume and additional explanatory variable(s)) were used to estimate the CMFs and only CMFs with lower standard error were recommended. The results of this study proved that safety effectiveness was significantly improved across all locations that were upgraded from Traditional Mainline Toll Plazas (TMTP) to the Hybrid Mainline Toll Plazas (HMTP) system. This treatment significantly reduced total, Fatal-and-Injury (F+I), and Rear-End crashes by 47, 46 and 65 percent, respectively. Moreover, this study examined the traffic safety impact of using different designs, and diverge-and-merge areas of the HMTP. This design combines either express Open Road Tolling (ORT) lanes on the mainline and separate traditional toll collection to the side (design-1), or traditional toll collection on the mainline and separate ORT lanes to the side (design-2). It was also proven that there is a significant difference between these designs, and there is an indication that design-1 is safer and the majority of crashes occurred at diverge-and-merge areas before and after these facilities. However, design-2 could be a good temporary design at locations that have low prepaid transponder (Electronic Toll Collection (ETC)) users. In other words, it is dependent upon the percentage of the ETC users. As this percentage increases, more traffic will need to diverge and merge; thus, this design becomes riskier. In addition, the results indicated significant relationships between the crash frequency and toll plaza types, annual average daily traffic, and drivers* age. The analysis showed that the conversion from TMTP to the All-Electronic Toll Collection (AETC) system resulted in an average reduction of 77, 76, and 67 percent for total, F+I, and Property Damage Only (PDO) crashes, respectively; for rear end and Lane Change Related (LCR) crashes the average reductions were 81 and 75 percent, respectively. The conversion from HMTP to AETC system enhanced traffic safety by reducing crashes by an average of 23, 29 and 19 percent for total, F+I, and PDO crashes; also, for rear end and LCR crashes, the average reductions were 15 and 21 percent, respectively. Based on these results, the use of AETC system changed toll plazas from the highest risk sections on Expressways to be similar to regular segments. Therefore, it can be concluded that the use of AETC system was proven to be an excellent solution to several traffic operations as well as environmental and economic problems. For those agencies that cannot adopt the HMTP and the AETC systems, improving traffic safety at traditional toll plazas should take a priority. This study also evaluates the safety effectiveness of the implementation of High-Occupancy Toll lanes (HOT Lanes) as well as adding roadway lighting to expressways. The results showed that there were no significant impact of the implementation of HOT lanes on the roadway segment as a whole (HOT and Regular Lanes combined). But there was a significant difference between the regular lanes and the HOT lanes at the same roadway segment; the crash count increased at the regular lanes and decreased at the HOT lanes. It was found that the total and F+I crashes were reduced at the HOT lanes by an average of 25 and 45 percent, respectively. This may be attributable to the fact that the HOT lanes became a highway within a highway. Moreover adding roadway lighting has significantly improved traffic safety on the expressways by reducing the night crashes by approximately 35 percent. Overall, the proposed analyses of the safety effectiveness of using different toll collection systems are useful in providing expressway authorities with detailed information on where countermeasures must be implemented. This study provided for the first time an up-to-date safety impact of using different toll collection systems, also developed safety guidelines for these systems which would be useful for practitioners and roadway users.