Stuttgart, Universiẗat, Diss., 2004.

It is estimated that over half of bicycle crashes are not reported. There are various reasons for this, such as no property damage or physical injuries sustained. In order to improve the likelihood that bicycle riders will report a crash, I have developed Urban Bike Buddy, a smartphone application which uses the internal sensors of the device to detect a crash. The application interacts with Alexa to help guide the user through the crash reporting process. The innovative features of my work are the ability to initiate communication with Alexa without user interaction. In addition, there is an intersection controller that has been connected to extra hardware that allows bicycle riders to request a crossing signal during their approach based on the speed that they are riding. These features add value to bicycle riders, and will help contribute to a safer environment for bicycle riders, automobiles, and pedestrians as well.

Thesis advisor: Harold Petersen / Minsky developed a Financial Instability Hypothesis which sought to find an endogenous explanation for a modern economy’s vulnerability to crashes. Specifically, he investigated the ways in which the financial structures of a modern economy might contribute to its instability. The hypothesis rests upon the twin assertions that some financial arrangements are more dangerous than others, and that during economic booms, investors’ incentives are altered to favor these more dangerous arrangements. Essentially, in good times, the profit-seeking motive of investors overrides a diminished risk aversion, as memories of losses fade into the past. This paper empirically tests Minsky’s second assertion, by using econometric techniques to analyze the relationship between risk appetite and market returns. Spreads between the yields of bonds of different credit qualities are used as a proxy for wider investor sentiment toward risk. Regressions demonstrate that changes in risk appetite can be explained at least in part by historical market returns. Such a finding supports Minsky’s proposal that incentives of investors change in response to varying market conditions. It further implies that regulatory authorities might examine the level of risk appetite to determine whether increases in asset prices indicate the formation of speculative bubbles or are rather reflecting developments in the fundamentals underlying said assets. / Thesis (BA) — Boston College, 2010. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Economics Honors Program. / Discipline: College Honors Program. / Discipline: Economics.

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xv, 393 p.; also includes graphics. Includes bibliographical references (p. 104-113).

Universiẗat, Diss., 2005--Stuttgart.

This thesis comprises a programme of work investigating the use of active human computer models and the effects of forthcoming automotive safety features on vehicle occupants; more specifically, their unbelted kinematics and sustained injuries. Since Hybrid III anthropometric crash test dummies are unable to replicate human occupant kinematics under severe braking, the thesis highlighted the need to research the most appropriate occupant computer model to simulate active safety scenarios. The first stage of the work focussed on occupant kinematics and developed unique human occupant reflex response target curves describing the head and torso relative angle change as a function of time, based on human volunteers’ low deceleration sled tests. These biomechanics curves were, subsequently, used to validate an active human model, asserting its torso response, while confirming that further development in its neck response was necessary. The sled test computer validation proved that only an active human model was suitable to model a pre-braking phase. The second stage of the work combined the occupant’s kinematics of the pre-braking phase, followed by a subsequent frontal crash into a rigid barrier inducing an airbag deployment. The results suggested that, in a 1g frontal deceleration pre-braking phase, the kinematics of an unbelted occupant within the vehicle compartment was complex and in some cases extreme. With the parameters adopted within this unique study, it was observed that occupant motion and position relative to the airbag system varied depending on awareness level, seat friction, braking duration and posture. Additionally, it was observed that a driver holding the steering wheel with one hand could be out of the airbag deployment reach due to extreme Out-Of-Position (OOP). Results also concluded that the dynamic OOP scenario was intricate and would yield to higher occupant injuries. Future studies, into brake dive, seat geometry, seat stiffness and cabin packaging, are recommended to capture the vehicle configuration providing the highest dynamic OOP safety risk. Finally, the investigations conducted, as part of this doctoral programme, led to the provision of new knowledge in the validation of active human models, a unique demonstration of the importance using human computer models, rather than crash test dummies, as well as the potential for the evaluation of future restraint systems in dynamics unbelted OOP, considering various posture scenarios.

La simulation numérique est de plus en plus utilisée dans l’industrie pour réduire le coût lié aux essais physiques. Une simulation de crash (préparation + soumission au solveur + traitement) dure environ une à deux journées. Renault utilise l’optimisation, donc de nombreuses simulations de crash, pour dimensionner ses véhicules. Afin de réduire le coût total d’un ensemble de simulations crash, le but de cette thèse est de proposer une ou des méthodes de réduction de modèle applicables dans un espace paramétrique. Les méthodes proposées dans cette thèse sont non-intrusives et n’obligent donc pas à modifier le solveur ni le modèle. La première méthode testée est la Proper Orthogonal Decomposition. Elle permet de réduire le comportement d’une simulation et de comprendre les propriétés du crash mais l’interpolation dans l’espace paramétrique est plus difficile. La deuxième méthode, ReCUR, est une variante de la décomposition CUR classique. Elle sera montrée comme une forme générale des méthodes non-intrusives. Elle permet de surmonter les deux limites importantes des méthodes de réduction actuelles : taille du modèle élevée et interpolation. / The numerical simulation is more and more applied in the industry in order to reduce the physical tests costs. A crash simulation (pre-processing, processing and post-processing) takes about one or two days. Renault uses the optimization, so numerous crash simulations, to size cars. To cut back the total cost of a whole crash simulations, the aim of this thesis is to propose a or some Reduced-Order Model (ROM) methods that can be applied in a parametric space. The suggested methods in this thesis are nonintrusive and neither the solver nor the model should not be modified . The first tested method is the Proper Orthogonal Decomposition. This method allows reducing the behavior of a crash simulation and understanding the crash properties but not interpolating in a parametric space. The second method, ReCUR, is a variant of the classical decomposition CUR. It will be demonstrated as a general form of the non-intrusive methods. It allows overcoming two important limits of actual ROM methods : size of the model and the interpolation.

Traffic crashes tend to occur at relatively greater frequencies at particular locations, at particular time periods, and for particular subsets of drivers and vehicles. It is well recognized among the road safety community that crash-risk is highly elevated when inclement weather conditions occur in the winter. To present, most of the road safety studies focus on event-based analysis or seasonal analysis and give little attention to explore high-risk conditions at the daily temporal scale. The purpose of the study is to advance our understanding of high-risk crash conditions at the daily level and their occurrences in Southern Ontario, Canada. The study explores different definitions of high-crash days, and quantifies the influences of weather conditions, risk exposure, months and timing of precipitation on the likelihood of a high-crash day occurring using binary logistic regression model. Additionally, an approach for estimating the relative risk exposure using available traffic count data has also been developed. The results of the study show a small proportion of high-crash days are responsible for a considerable amount of traffic crashes during the winter. The risk of traffic crash is twice as high on high-crash days in comparison to non-high-crash days. The modeling approach well-fits the data and shows that winter weather conditions have significant influence on high-crash days with results being mostly consistent across the four study areas, Toronto, the Area Surrounding Toronto, London and the Area Surrounding London. Low temperature, heavy snowfalls, high wind speeds, high traffic volumes, early winter months, occurrence of precipitation in both morning and evening increase the odds of high-crash days to a large extent. The results of study could help to pre-schedule traffic operation and enforcement, to effectively distribute road safety resources and personnel, and to create situational awareness among road users and other stakeholders.

Traffic crashes tend to occur at relatively greater frequencies at particular locations, at particular time periods, and for particular subsets of drivers and vehicles. It is well recognized among the road safety community that crash-risk is highly elevated when inclement weather conditions occur in the winter. To present, most of the road safety studies focus on event-based analysis or seasonal analysis and give little attention to explore high-risk conditions at the daily temporal scale. The purpose of the study is to advance our understanding of high-risk crash conditions at the daily level and their occurrences in Southern Ontario, Canada. The study explores different definitions of high-crash days, and quantifies the influences of weather conditions, risk exposure, months and timing of precipitation on the likelihood of a high-crash day occurring using binary logistic regression model. Additionally, an approach for estimating the relative risk exposure using available traffic count data has also been developed. The results of the study show a small proportion of high-crash days are responsible for a considerable amount of traffic crashes during the winter. The risk of traffic crash is twice as high on high-crash days in comparison to non-high-crash days. The modeling approach well-fits the data and shows that winter weather conditions have significant influence on high-crash days with results being mostly consistent across the four study areas, Toronto, the Area Surrounding Toronto, London and the Area Surrounding London. Low temperature, heavy snowfalls, high wind speeds, high traffic volumes, early winter months, occurrence of precipitation in both morning and evening increase the odds of high-crash days to a large extent. The results of study could help to pre-schedule traffic operation and enforcement, to effectively distribute road safety resources and personnel, and to create situational awareness among road users and other stakeholders.

Zugl.: Bochum, Univ., Diss., 2009