The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

Many developing countries have witnessed fast and rapid growth in the last two decades due to the high development rate of economic activity in these countries. Many transportation projects have been constructed. In the same time both population growth and vehicle ownership rate increased; resulting in increasing levels of road crashes. Road traffic crashes in Gulf Cooperation Council (GCC) is considered a serious problem that has deep effects on GCC's population as well as on the national productivity through the loss of lives, injuries, property damage and the loss of valuable resources. From a recent statistical study of traffic crashes in Oman, it was found that in 2013 there were 7,829 crashes occurred for a total of 1,082,996 registered vehicles. These crashes have resulted in 913, 5591, and 1481 fatal, injury and property damage only crashes, respectively (Directorate General of Traffic, 2014), which is considered high rates of fatalities and injuries compared to other more developed countries. This illustrates the seriousness and dangerousness of the safety situation in GCC countries and Oman particularly. Thus, there is an urgent need to alleviate the Severity of the traffic safety problem in GCC which in turn will set a prime example for other developing countries that face similar problems. Two main data sources from Riyadh, the capital city of Kingdom of Saudi Arabia (KSA) and Muscat, the capital city of Sultanate of Oman have been obtained, processed, and utilized in this study. The Riyadh collision and traffic data for this study were obtained in the form of crash database and GIS maps from two main sources: the Higher Commission for the Development of Riyadh (HCDR) and Riyadh Traffic Department (RTD). The Muscat collision and traffic data were obtained from two main sources: the Muscat Municipality (MM) and Royal Oman Police, Directorate General of Traffic (DGC). Since the ARC GIS is still not used for traffic crash geocoding in Oman, the crash data used in the analysis were extracted manually from the filing system in the DGC. Due to the fact that not all developing countries highway agencies possess sufficient crash data that enable the development of robust models, this problem gives rise to the interest of transferability of many of the models and tools developed in the US and other developed nations. The Highway Safety Manual (HSM) is a prime and comprehensive resource recently developed in the US that would have substantial impact if researchers are able to transfer its models to other similar environment in GCC. It would save time, effort, and money. The first edition of the HSM provides a number of safety performance functions (SPFs), which can be used to predict collisions on a roadway network. This dissertation examined the Transferability of HSM SPFs and developing new local models for Riyadh and Muscat. In this study, first, calibration of the HSM SPFs for Urban Four-lane divided roadway segments (U4D) with angle parking in Riyadh and the development of new SPFs were examined. The study calibrates the HSM SPFs using HSM default Crash Modification Factors (CMFs), then new local CMFs is proposed using cross-sectional method, which treats the estimation of calibration factors using fatal and injury data. In addition, new forms for specific SPFs are further evaluated to identify the best model using the Poisson-Gamma regression technique. To investigate how well the safety performance model fits the data set, several performance measures were examined. The performance measures summarize the differences between the observed and predicted values from related SPFs. Results indicate that the jurisdiction-specific SPFs provided the best fit of the data used in this study, and would be the best SPFs for predicting severe collisions in the City of Riyadh. The study finds that the HSM calibration using Riyadh local CMFs outperforms the calibration method using the HSM default values. The HSM calibration application for Riyadh crash conditions highlights the importance to address variability in reporting thresholds. One of the findings of this research is that, while the medians in this study have oversize widths ranging from 16ft-70ft, median width has insignificant effect on fatal and injury crashes. At the same time the frequent angle parking in Riyadh urban road networks seems to increase the fatal and injury collisions by 52 percent. On the other hand, this dissertation examined the calibration of the HSM SPFs for Urban intersections in Riyadh, Kingdom of Saudi Arabia (KSA) and the development of new set of models using three year of collision data (2004-2006) from the city of Riyadh. Three intersection categories were investigated: 3-leg signalized, 4-leg signalized, and 3-leg unsignalized. In addition, new forms for specific SPFs are further evaluated to identify the best model using the Poisson-Gamma regression technique. Results indicate that the new local developed SPFs provided the best fit of the data used in this study, and would be the best SPFs for predicting severe crashes at urban intersections in the City of Riyadh Moreover, this study examined the calibration of the HSM SPFs for Fatal and Injury (FI), Property Damage Only (PDO) and total crashes for Urban Four-lane divided roadway segments (U4D) in Muscat, Sultanate of Oman and the development of new SPFs. This study first calibrates the HSM SPFs using the HSM methodology, and then new forms for specific SPFs are further evaluated for Muscat's urban roads to identify the best model. Finally, Riyadh fatal and injury model were validated using Muscat FI dataset. Comparisons across the models indicate that HSM calibrated models are superior with a better model fit and would be the best SPFs for predicting collisions in the City of Muscat. The best developed collision model describes the mean crash frequency as a function of natural logarithm of the annual average daily traffic, segment length, and speed limit. The study finds that the differences in road geometric design features and FI collision characteristics between Riyadh and Muscat resulted in an un-transferable Riyadh crash prediction model. Overall, this study lays an important foundation towards the implementation of HSM methods in multiple cities (Riyadh and Muscat), and could help their transportation officials to make informed decisions regarding road safety programs. The implications of the results are extendible to other cities and countries and the region, and perhaps other developing countries as well.

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.