Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts. / Doctor of Philosophy / Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts.

Understanding safety factors in construction is critical to reducing accident frequency and severity. Grounded in the safety performance model, this study was conducted to examine the impact of psychological capital (PsyCap), which consists of the shared variance of hope, efficacy, resilience and optimism, and Past, Present, and Future thinking perspectives, on safety climate and performance. A nonexperimental quantitative design was used to determine whether PsyCap and thinking perspectives of construction project employees predicted safety performance and/or moderated the relationship between safety climate and performance across construction sites in different countries. 411 construction employees were recruited via a multistage and clustering strategy and took part in the study. The PsyCap Questionnaire, MindTime Profile Inventory, Group-Level Safety Climate Scale, and Safety Performance Measure were used to assess PsyCap, thinking perspectives, safety climate, and safety performance. Multiple regression was used to determine the effects of PsyCap and thinking perspectives on safety climate and safety performance. Results showed that Future and Present thinking predicted PsyCap as well as safety climate and safety performance in the construction industry. Further, safety performance accounted for variations in hope and efficacy, two PsyCap components. Findings might be used to influence thinking perspectives of team leaders in designing training, developing employees' efficacy levels, and preventing accidents and fatalities on construction sites.

Occupational stressors have been extensively studied as predictors of safety performance and employee well-being in previous research. However, many newly introduced organizational constructs that have the characteristics of an occupational stressor have rarely been studied as such, especially from a within-person perspective. The current study focused on three occupational stressors in relation to safety performance. Based on previous literature, I proposed that within individuals, compulsory citizenship behavior, illegitimate tasks, and interpersonal conflict at work as occupational stressors would have negative effects on employees well-being and safety performance through negative emotions (anger), job attitudes (job satisfaction and organizational commitment) and role stressors (role conflict and role ambiguity). In addition, reception of organizational citizenship behavior (ROCB) and perceived safety climate were hypothesized to moderate the relationships of the three occupational stressors with safety performance and employee well-being. Seventy-one nurses were recruited, and data were collected from their survey responses about their daily experiences on the focal variables for 9 shifts over three consecutive working weeks. Results showed that within individuals, the three occupational stressors were positively associated with employee burnout and physical symptoms, and evidence was found that those associations might be mediated by anger, job satisfaction and role conflict. Further, ROCB was found to moderate some of the associations of occupational stressors with safety performance and employee well-being. However, the current study failed to find support for any of the hypotheses regarding perceived safety performance as a moderator in this sample. Findings, limitations and future directions were discussed.

Cyclists as vulnerable road users are oftentimes unprotected with exposed human body, can fall easily and sustain serious injuries when encountered collisions, especially with motorists. At roundabouts, accident reduction rate for cyclists is rather uncertain or sometimes less favorable compared to other road users (e.g. motorists, pedestrians). This thesis focuses on advancing the understanding of traffic safety issues for cyclists at roundabouts by identifying concerns faced by cyclists and evaluating their designs to find out which configuration has high or higher safety level towards cyclists. The research approach adopted in this work includes a wide review of relevant literature on cyclist safety and roundabouts and the implementation of empirical research, the latter was carried out through a Case Study in Norrköping city by obtaining cyclist related accident data from Swedish Traffic Accident Data Acquisition database to identify roundabouts with high cyclist-related accidents in the city assisted with PTV Visum Safety tool and fetching traffic volume from city network model operated by Norrköping Municipality for each identified roundabout. The main findings from this research conclude that single-lane roundabouts with separated cycle paths in high traffic volume setting provide better or higher safety performance for cyclists compared to other roundabout configurations.

Work-related accidents emerge from potential hazards that can cause different negative outcomes in different situations. Human errors are specific actions that can either directly (active errors) or indirectly (latent errors) cause an accident in the workplace. Nowadays in order to establish an applicable system in the way of maintenance and preferment of a work environment without any accidents that are trying to develop the HSE system. In fact, this management system has been using as a significant tool to control and improve the performance of health and safety and the environment in all development programs of industries and organizations. In this term, one of the important perspectives of HSE management is Emotional Intelligence which deals with the management’s ability and safety performance in the workplace. The purpose of this study is to investigate the relationship between Emotional Intelligence and Occupational Health, Safety and Environment (HSE) management in the workplace, to reduce industrial incidents of human factors. Therefore, there is a requirement for a better understanding of how Emotional Intelligence factors influence health and safety performance in the workplace. A qualitative study has been done to achieve this purpose. In this case, data has been collected through eight semi-structured interviews with HSE managers and officers that participated from different industries around the world.  The main focus of this collection data was extracting the perspectives of the individual’s views. Afterward, to create a theory, the data has been analyzed according to different steps for a grounded analysis regarding discovering how the Emotional Intelligence factors of employees impact their health and safety performance in the workplace.   The results of this study have shown that there are mainly two areas to study which are key roles of Emotional Intelligence in safety performance and key roles for effective Healthy, Safety, and Environment management. It has shown that the key roles of Emotional Intelligence in safety performance is being able to manage your own and being able to deal with other’s emotions. Besides, key roles for effective Healthy, Safety, and Environment management only three factors have been important to improve the safety act which is being able to make the correct decision in the emergency situation’ and ability to prevent incidents at the workplace as well as the level of perception of risk. These results demonstrated that strong factors of Emotional Intelligence are vital to improve the health and safety performance at the workplace and the improvement of these abilities should be approached for the workplace.

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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 thesis comprises two studies that investigated bike infrastructure safety in North America. The first study conducted a corridor-level analysis to quantify the safety of different bike infrastructure types and assess the influence of corridor-specific characteristics on their performance. Using a Poisson-lognormal Full Bayesian model, the study analyzed fatal and injury bike-vehicle collisions on over 7800 corridors in Toronto, Canada. The findings revealed that bike infrastructure effectively reduces bike collisions, with cycle tracks demonstrating superior safety benefits due to the physical separation of cyclists from vehicular traffic. Cycle tracks were found to be particularly effective on long corridors with fewer intersections as bike-vehicle interactions along the corridor are minimized. Signed routes were safe on low-volume and low-speed roads, while bike lanes are more suited for a short section of high-volume corridors with a high intersection density. The second study assessed the safety of parking-protected bike lanes (PPBL), a new concept that is rapidly growing in North America. Utilizing data from nineteen street sections in Vancouver and Ottawa, the study developed a Full Bayesian Before-and-after model to evaluate the safety impacts of converting traditional painted bike lanes to PPBLs. The results indicated a significant reduction of 31.2% in total collisions after PPBL implementation. However, the effects of PPBLs on cyclist safety were found to be sensitive to factors such as bike path opening density, intersection density, and intersection treatments. In roads where proper intersection treatments and minimal protection of PPBL openings can be provided, painted bike lanes can be converted into PPBLs, and significant safety benefits can be expected. The findings of this thesis offer practical guidance for city planners and policymakers regarding the safety implications of different bike infrastructure types and the most appropriate conditions to implement them, which supports bike safety enhancement initiatives and attracts more people to bike. / Thesis / Master of Applied Science (MASc) / This thesis presents two studies that offer valuable insights to improve bike safety. The first study examined the safety of various bike infrastructure types along with the impact of corridor characteristics on their performance. The findings emphasized the effectiveness of cycle tracks in reducing collisions on long corridors with fewer intersections. Signed routes were found to be effective on low-volume and low-speed roads, while bike lanes were ideal on short sections of high-volume roads with a high intersection density. The second study assessed the impacts of new designed concept, known as the parking-protected bike lanes (PPBLs). The study showed that converting painted bike lanes to PPBLs significantly reduced total collisions. However, proper treatment of intersection and bikeway openings is crucial for enhancing cyclist safety and reducing multi-vehicle rear-end collisions. Where proper intersection treatment and minimal protection of bikeway openings can be provided, bike lanes can be safely converted into PPBLs.