In 2014, 32,675 deaths were recorded in vehicle crashes within the United States. Out of these, 51% of the fatalities occurred in rural highways compared to 49% in urban highways. No specific crash data are available for the built-up areas along rural highways. Due to high fatalities in rural highways, it is important to identify the factors that cause the vehicle crashes. The main objective of this study is to determine the factors associated with severities of crashes that occurred in built-up areas along the rural highways of Nevada. Those factors could aid in making informed decisions while setting up speed zones in these built-up areas. Using descriptive statistics and binary logistic regression model, 337 crashes that occurred in 11 towns along the rural highways from 2002 to 2010 were analyzed. The results showed that more crashes occurred during favorable driving conditions, e.g., 87% crashes on dry roads and 70% crashes in clear weather. The binary logistic regression model showed that crashes occurred from midnight until 4 a.m. were 58.3% likely to be injury crashes rather than property damage only crashes, when other factors were kept at their mean values. Crashes on weekdays were three times more likely to be injury crashes than that occurred on weekends. When other factors were kept at their mean value, crashes involving motorcycles had an 80.2% probability of being injury crashes. Speeding was found to be 17 times more responsible for injury crashes than mechanical defects of the vehicle. As a result of this study, the Nevada Department of Transportation now can take various steps to improve public safety, including steps to reduce speeding and encourage the use of helmets for motorcycle riders.

In December 1978, United Airlines Flight 173 arriving in Portland from Denver with 189 people aboard crash-landed in a suburb at 157th and East Burnside. Ten people were killed and dozens more were injured. The jet ran out of fuel after it had circled for an hour while the crew tried to determine what was wrong with the right main landing gear, which had fallen with a huge double jolt on extension. The investigation that followed the crash placed the blame squarely on the pilot for his negligence in failing to monitor his fuel supply, and secondarily on his crew members, who failed to adequately communicate their concerns about it. The accident was a watershed event in what would become known in the airline industry as crew resource management, a communication model designed to reduce human error by fostering collaborative decision-making and assertiveness training. In the years that have followed the accident, very little has changed in the narrative surrounding it. Articles and docudramas on the plane crash consistently repeat the tale as is, blaming the pilot and shedding no light on the factors that led to the in-flight emergency or on United's role in contributing to the crash. This thesis is a "cold-case" investigation that reveals those contributing factors, which have been so thoroughly ignored. In the words of renowned attorney F. Lee Bailey, "The rule of law requires that all parties who contribute to an accident share in the responsibility for whatever harm has been caused." This is the untold story of all the decisions that brought down United Flight 173, and of the responsibilities heretofore overlooked.

The global pandemic of Covid-19 led to a crisis of not only fatal impact but financially. The stock market experienced one of its biggest market crashes ever and the young investors of our generation experienced something that they could never imagine. Their financial situation and investments suddenly changed and many of them did not know how to act or behave during this difficult period. This paper is based on qualitative research where ten different young investors have been interviewed to determine if they have matured and how their behavior has changed financially. The two research questions for the paper are: Is Covid-19 a blessing in disguise forcing young investors to mature, increasing their financial literacy, and thus change their investment behavior? and how has the Covid-19 pandemic affected young investors decision making on the stock market? The study and research questions were determined using three theories which are the efficient market hypothesis, behavioral finance, and the stages of change model. All three models were used to see how an individual behave during a financial crisis. The efficient market hypothesis basically argues that all relevant information matches the prices of stocks at any given time. Behavioral finance examines how an individual behave in a financial setting such as investment behaviors. Herding behavior and overconfidence are two cognitive biases within behavioral finance that was easily seen in the individuals for this study. The stages of change model states that an individual go through five different stages when a change of behavior is needed. This model could be applied on any individual, but a change of investments behavior should not go through so many stages as the model has so it had some limitations on the individuals in the study. The results and findings of the paper states that young investors have learned a valuable lesson from the Covid-19 pandemic increasing their financial literacy and creating more sustainable strategies for future investments.

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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)

Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.

Road crashes pose a significant public health issue worldwide, often leading to severe injuries and fatalities. This dissertation embarks on a comprehensive examination of the factors affecting road crash severity, with a special focus on older drivers and the unique challenges introduced by the COVID-19 pandemic. Utilizing a dataset from Virginia, USA, the research integrates advanced statistical methods and machine learning techniques to dissect this critical issue from multiple angles. The initial study within the dissertation employs multilevel ordinal logistic regression to assess crash severity among older drivers, revealing the complex interplay of various factors such as crash type, road attributes, and driver behavior. It highlights the increased risk of severe crashes associated with head-on collisions, driver distraction or impairment, and the non-use of seat belts, specifically affecting older drivers. These findings are pivotal in understanding the unique vulnerabilities of this demographic on the road. Furthermore, the dissertation explores the efficacy of both parametric and non-parametric machine learning models in predicting crash severity. It emphasizes the innovative use of synthetic resampling techniques, particularly random over-sampling examples (ROSE) and synthetic minority over-sampling technique (SMOTE), to address class imbalances. This methodological advancement not only improves the accuracy of crash severity predictions for severe crashes but also offers a comprehensive understanding of diverse factors, including environmental and roadway characteristics. Additionally, the dissertation examines the influence of the COVID-19 pandemic on road safety, revealing a paradoxical decrease in overall traffic crashes accompanied by an increase in the rate of severe injuries. This finding underscores the pandemic's transformative effect on driving behaviors and patterns, heightening risks for vulnerable road users like pedestrians and cyclists. The study calls for adaptable road safety strategies responsive to global challenges and societal shifts. Collectively, the studies within this dissertation contribute substantially to transportation safety research. They demonstrate the complex nature of factors influencing crash severity and the efficacy of tailored approaches in addressing these challenges. The integration of advanced statistical methods with machine learning techniques offers a profound understanding of crash dynamics and sets a new benchmark for future research in transportation safety. This dissertation underscores the evolving challenges in road safety, especially amidst demographic shifts and global crises, and advocates for adaptive, evidence-based strategies to enhance road safety for all, particularly vulnerable groups like the older drivers. / Doctor of Philosophy / Road crashes are a major concern worldwide, often leading to serious injuries and loss of life. This dissertation delves into the critical issue of road crash severity, with a special focus on older drivers and the challenges brought about by the COVID-19 pandemic. Drawing on data from Virginia, USA, the research combines cutting-edge statistical methods and machine learning to shed light on this pressing matter. One important part of the research focuses on older drivers. It uses advanced analysis to find out why crashes involving this group might be more serious. The study discovered that situations like head-on collisions, driver distraction or impairment, and not wearing seat belts greatly increase the risk for older drivers. Understanding these risks is crucial in identifying the special needs of older drivers on the road. Then, the study explores the power of machine learning in predicting crash severity. Here, the research stands out by using innovative techniques to balance out the data, leading to more accurate predictions. This part of the study not only improves our understanding of what leads to severe crashes but also highlights how different environmental and road factors play a role. Following this, the research looks at how the COVID-19 pandemic has impacted road safety. Interestingly, while the overall number of crashes went down during the pandemic, the rate of severe injuries in the crashes that occurred increased. This suggests that the pandemic changed driving behaviors, posing increased risks especially to pedestrians and cyclists. In summary, this dissertation offers valuable insights into the complex factors affecting road crash severity. It underscores the importance of using advanced analysis techniques to understand these dynamics better, especially in the face of demographic changes and global challenges like the pandemic. The findings are not just academically significant; they provide practical guidance for policymakers and road safety experts to develop strategies that make roads safer for everyone, particularly older drivers.

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Thesis (M.Med. (Forensic Pathology)) -- University of Limpopo, 2022 / Introduction and background: Road traffic fatalities remain a worldwide burden with more than half of those fatalities comprising of vulnerable road users (pedestrians, cyclists and motorcyclists). This prompted the World Health Organization and United Nations to establish Sustainable Developmental Goals aimed at reducing road traffic crashes. The study explored factors relating to pedestrian fatalities on the R71 road, which may inform future interventions to enhance pedestrian safety. Aim: The study aimed at profiling pedestrian road traffic crash fatalities on the R71 road admitted at Polokwane Forensic Pathology Services. Methodology: A quantitative descriptive study utilising total population purposive sampling of pedestrians that demised because of R71 road traffic crashes over a 3-year period was done. There were 65 cases studied. Results: The study revealed that the fatalities were more male adult pedestrians than females, who were between 20 -39 years old. Majority of those pedestrians were wearing dark coloured clothing with no reflectors on. The pedestrian fatalities were mostly seen over the weekend and between evening and midnight. The fatalities peaked in December and February (summer season). The common locality of the pedestrian fatalities was Mankweng and Mentz village (Area 3). Most of those pedestrians sustained head injuries. Conclusion: Contributory factors and injuries of those pedestrian fatalities that demised because of R71 road traffic crashes were identified, which some were similar to those already highlighted in literature.