Recent technological innovations and the need for increased safety on the world's roads have led to the introduction of In- Vehicle Information Systems (IVIS). These systems will provide navigation and advisory information to drivers while they are driving. One aspect of these systems, In-vehicle Signing Information Systems (ISIS), would provide the warning, regulatory, and advisory information that is currently found on road signs. These systems may be of particular benefit when external elements such as rain, snow, or night driving reduce or eliminate the opportunity for drivers to detect road signs. This study attempts to determine what benefits, if any, are realized by drivers using this system. Fifty-eight drivers operated an instrumented Oldsmobile Aurora under eight conditions. The eight conditions consisted of a daylight-clear weather-ISIS condition, a daylight-clear weather-No ISIS condition, a daylight-rain-ISIS condition, a daylight-rain-No ISIS condition, a night-clear weather-ISIS condition, a night-clear weather-No ISIS condition, a night-rain-ISIS condition, and a night-rain-No ISIS condition. Younger drivers (18-30 years old) and older drivers (65 years or older) took part in this study. Three measures of driver performance were collected along with subjective preference data. Each measure was evaluated in order to determine what impact, if any, weather, time of day, age, and ISIS use had on performance. Subjective data was evaluated to determine driver preference and acceptance of the ISIS display. The results indicated that use of the ISIS display led to reduced speeds and greater reaction distances for all drivers. Evidence was found that seems to indicate that older drivers may receive a greater benefit in complex, unfamiliar, or low visibility situations. Evidence was also found that indicates that all drivers may receive a greater benefit at night for the complex or unfamiliar situations. Subjectively, the majority of the drivers indicated that the ISIS display made them more aware of road sign information. / Master of Science

Although gas turbine engines are designed to use dry air as the working fluid, the great demand over the last decades for air travel at several altitudes and speeds has increased aircraft’s exposure to inclement weather conditions. Although, they are required to perform safely under the effect of various meteorological phenomena, in which air entering the engine contains water, several incidents have been reported to the aviation authorities about power loss during flight at inclement weather. It was understood that the rain ingestion into a gas turbine engine influences the performance of the engine and particular the compressor and the combustor. The effects of water ingestion on gas turbine engines are aerodynamic, thermodynamic and mechanical. These effects occur simultaneously and affect each other. Considering the above effects and the fact that they are timedependent, there are few gas turbine performance simulation tools, which take into account the water ingestion phenomenon. This study is a new research of investigating theoretically the water ingestion effects on a gas turbine performance. It focuses on the aerodynamic and mechanical effects of the phenomenon on the compressor and the combustor. The application of Computational Fluid Dynamics (CFD) is the basic methodology to examine the details of the flow in an axial compressor and how it is affected by the presence of water. The calculations of water film thickness, which is formed on the rotor blade, its motion (direction and speed) and the extra torque demand, are provided by a code created by the author using FORTRAN programming language. Considering the change in blade’s profile and the wavy characteristics of the liquid film, the compressor’s performance deterioration is calculated. The compressor and combustor’s deterioration data are imported to a gas turbine simulation code, which is upgraded to calculate overall engine’s performance deterioration. The results show a considerable alteration in engine’s performance parameters and arrive at the same conclusions with the relevant experimental observations.

Although gas turbine engines are designed to use dry air as the working fluid, the great demand over the last decades for air travel at several altitudes and speeds has increased aircraft’s exposure to inclement weather conditions. Although, they are required to perform safely under the effect of various meteorological phenomena, in which air entering the engine contains water, several incidents have been reported to the aviation authorities about power loss during flight at inclement weather. It was understood that the rain ingestion into a gas turbine engine influences the performance of the engine and particular the compressor and the combustor. The effects of water ingestion on gas turbine engines are aerodynamic, thermodynamic and mechanical. These effects occur simultaneously and affect each other. Considering the above effects and the fact that they are timedependent, there are few gas turbine performance simulation tools, which take into account the water ingestion phenomenon. This study is a new research of investigating theoretically the water ingestion effects on a gas turbine performance. It focuses on the aerodynamic and mechanical effects of the phenomenon on the compressor and the combustor. The application of Computational Fluid Dynamics (CFD) is the basic methodology to examine the details of the flow in an axial compressor and how it is affected by the presence of water. The calculations of water film thickness, which is formed on the rotor blade, its motion (direction and speed) and the extra torque demand, are provided by a code created by the author using FORTRAN programming language. Considering the change in blade’s profile and the wavy characteristics of the liquid film, the compressor’s performance deterioration is calculated. The compressor and combustor’s deterioration data are imported to a gas turbine simulation code, which is upgraded to calculate overall engine’s performance deterioration. The results show a considerable alteration in engine’s performance parameters and arrive at the same conclusions with the relevant experimental observations.

A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs. Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers. The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs. A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace.

Vehicle crashes remain the leading cause of accidental death and injuries in the United States, claiming tens of thousands of lives and injuring millions of people each year. Many of these crashes occur during nighttime, where a variety of modifiers affect the risk of a crash, primarily through the reduction of object visibility. Furthermore, many of these modifiers also affect the nighttime mobility of older drivers, who avoid driving during the nighttime. Thus, a two-fold need exists for new technologies that enhance night visibility. Two separate studies were completed as part of this research. Study 1 served as a baseline by evaluating visual performance during nighttime driving under clear weather conditions. Visual performance was evaluated in terms of the detection and recognition distances obtained when different vision enhancement systems were used at the Smart Road testing facility. Study 2, also using detection and recognition distances, compared the visual performance of drivers during low visibility conditions (i.e., due to rain) to the risk perception of driving during nighttime under low visibility conditions. These comparisons were made as a function of various vision enhancement systems. The age of the driver and the characteristics of the object presented (e.g., contrast, motion) were variables of interest in both studies. The pivotal contribution of this investigation is the generation of a model describing the relationships between driver characteristics, risk perception, and visual performance in nighttime driving in the context of a variety of standard and prototype vision enhancement systems. Improvement of mobility, especially for older individuals, can be achieved through better understanding of the factors that increase risk perception, identification of systems that improve detection and recognition distances, and consideration of drivers' opinions on possible solutions that improve nighttime driving safety. In addition, this research effort empirically described the night vision enhancement capabilities of 12 different vision enhancement systems during clear and adverse weather environments. / Ph. D.

Rainfall affects the performance of traffic operations and endangers safety. A common and conventional method (rain gauges) for rainfall measurements mostly provide precipitation records in hourly and 15-minute intervals. However, reliability, continuity, and wide area coverage pose challenges with this data collection method. There is also a greater likelihood for data misrepresentation in areas where short duration rainfall is predominant, i.e., reported values may not reflect the actual equivalent rainfall intensity during subintervals over the entire reporting period. With recent weather and climate patterns increasing in severity, there is a need for a more effective and reliable way of measuring rainfall data used for traffic analyses. This study deployed the use of precipitation radar data to investigate the spatiotemporal effect of rainfall on freeways in Jacksonville, Florida. The linear regression analysis suggests a speed reduction of 0.75%, 1.54%, and 2.25% for light, moderate, and heavy rainfall, respectively. Additionally, headways were observed to increase by 0.26%, 0.54%, and 0.79% for light, moderate, and heavy rainfall, respectively. Measuring precipitation from radar data in lieu of using rain gauges has potential for improving the quality of weather data used for transportation engineering purposes. This approach addresses limitations experienced with conventional rain data, especially since conventional collection methods generally do not reflect the spatiotemporal distribution of rainfall.

Adverse weather is considered as one of the important factors contributing to injuries and severe crashes. During rainy conditions, it can reduce travel visibility, increase stopping distance, and create the opportunity hydroplaning. This study quantified the relative crash risk on Oregon 217 southbound direction under rainy conditions by using a match-paired approach, applied one-year traffic data, crash data and NEXRAD Level II radar weather data. There are 26 crashes occurred in match-paired weather conditions for Oregon 217 in year 2007. The results of this study indicate that a higher crash risk and a higher property-damage-only crash risk occurred during rainy days. The crash risk level varies by the location of the highway, at milepost 2.55 station SW Allen Blvd has the highest driving risks under rainy conditions.

<p> It has been hypothesized that some measurable properties of $Z_{DR}$ arcs in supercells may change in the minutes prior to tornadogenesis and tornadogenesis failure, and that $Z_{DR}$ arc area will change with SRH and can be used as a real-time proxy to estimate SRH. Output form the Cloud Model 1 (CM1) along with a polarimetric emulator is used to simulate $Z_{DR}$ arcs in 9 tornadic and 9 non-tornadic supercells. A random forest algorithm is used to automatically identify the $Z_{DR}$ arcs. Finally the inflow sector SRH is calculated at times when $Z_{DR}$ arcs are identified. To analyze the change in intensity and area a comparison between the average $Z_{DR}$ value inside and outside of the arc, as well as the spatial size of the arc and storm was done. Model calculated SRH is then compared to these metrics.</p> <p> </p> <p> It has also been observed that hail fallout complicates the automatic identification of $Z_{DR}$ arcs. In this study, three experiments are run where the simulated $Z_{DR}$ arcs are produced. One using all categories of hydrometeors, one where wet growth and melting of hail is excluded, and one excluding the contribution to $Z_{DR}$ from the hail hydrometeor category. The same analysis as above is repeated for all three experiments. Finally observed $Z_{DR}$ arcs are analyzed to see if these results are applicable to the real world. </p>

A new approach to the evaluation of the reliability of electrical systems is presented. In this approach a Graph Trace Analysis based approach is applied to integrated system models and reliability analysis. The analysis zones are extended from the traditional power system functional zones. The systems are modeled using containers with iterators, where the iterators manage graph edges and are used to process through the topology of the graph. The analysis provides a means of computationally handling dependent outages and cascading failures. The effects of adverse weather, time-varying loads, equipment age, installation environment, operation conditions are considered. Sequential Monte Carlo simulation is used to evaluate the reliability changes for different system configurations, including distributed generation and transmission lines. Historical weather records and loading are used to update the component failure rates on-the-fly. Simulation results are compared against historical reliability field measurements. Given a large and complex plant to operate, a real-time understanding of the networks and their situational reliability is important to operational decision support. This dissertation also introduces using an Integrated System Model in helping operators to minimize real-time problems. A real-time simulation architecture is described, which predicts where problems may occur, how serious they may be, and what is the possible root cause. / Ph. D.

Les systèmes de vision artificielle sont de plus en plus présents en contexte routier. Ils sont installés sur l'infrastructure, pour la gestion du trafic, ou placés à l'intérieur du véhicule, pour proposer des aides à la conduite. Dans les deux cas, les systèmes de vision artificielle visent à augmenter la sécurité et à optimiser les déplacements. Une revue bibliographique retrace les origines et le développement des algorithmes de vision artificielle en contexte routier. Elle permet de démontrer l'importance des descripteurs d'images dans la chaîne de traitement des algorithmes. Elle se poursuit par une revue des descripteurs d'images avec une nouvelle approche source de nombreuses analyses, en les considérant en parallèle des applications finales. En conclusion, la revue bibliographique permet de déterminer quels sont les descripteurs d'images les plus représentatifs en contexte routier. Plusieurs bases de données contenant des images et les données météorologiques associées (ex : pluie, brouillard) sont ensuite présentées. Ces bases de données sont innovantes car l'acquisition des images et la mesure des conditions météorologiques sont effectuées en même temps et au même endroit. De plus, des capteurs météorologiques calibrés sont utilisés. Chaque base de données contient différentes scènes (ex: cible noir et blanc, piéton) et divers types de conditions météorologiques (ex: pluie, brouillard, jour, nuit). Les bases de données contiennent des conditions météorologiques naturelles, reproduites artificiellement et simulées numériquement. Sept descripteurs d'images parmi les plus représentatifs du contexte routier ont ensuite été sélectionnés et leur robustesse en conditions de pluie évaluée. Les descripteurs d'images basés sur l'intensité des pixels ou les contours verticaux sont sensibles à la pluie. A l'inverse, le descripteur de Harris et les descripteurs qui combinent différentes orientations sont robustes pour des intensités de pluie de 0 à 30 mm/h. La robustesse des descripteurs d'images en conditions de pluie diminue lorsque l'intensité de pluie augmente. Finalement, les descripteurs les plus sensibles à la pluie peuvent potentiellement être utilisés pour des applications de détection de la pluie par caméra.Le comportement d'un descripteur d'images en conditions météorologiques dégradées n'est pas forcément relié à celui de la fonction finale associée. Pour cela, deux détecteurs de piéton ont été évalués en conditions météorologiques dégradées (pluie, brouillard, jour, nuit). La nuit et le brouillard sont les conditions qui ont l'impact le plus important sur la détection des piétons. La méthodologie développée et la base de données associée peuvent être utilisées à nouveau pour évaluer d'autres fonctions finales (ex: détection de véhicule, détection de signalisation verticale).En contexte routier, connaitre les conditions météorologiques locales en temps réel est essentiel pour répondre aux deux enjeux que sont l'amélioration de la sécurité et l'optimisation des déplacements. Actuellement, le seul moyen de mesurer ces conditions le long des réseaux est l'installation de stations météorologiques. Ces stations sont coûteuses et nécessitent une maintenance particulière. Cependant, de nombreuses caméras sont déjà présentes sur le bord des routes. Une nouvelle méthode de détection des conditions météorologiques utilisant les caméras de surveillance du trafic est donc proposée. Cette méthode utilise des descripteurs d'images et un réseau de neurones. Elle répond à un ensemble de contraintes clairement établies afin de pouvoir détecter l'ensemble des conditions météorologiques en temps réel, mais aussi de pourvoir proposer plusieurs niveaux d'intensité. La méthode proposée permet de détecter les conditions normales de jour, de nuit, la pluie et le brouillard. Après plusieurs phases d'optimisation, la méthode proposée obtient de meilleurs résultats que ceux obtenus dans la littérature, pour des algorithmes comparables. / Computer vision systems are increasingly being used on roads. They can be installed along infrastructure for traffic monitoring purposes. When mounted in vehicles, they perform driver assistance functions. In both cases, computer vision systems enhance road safety and streamline travel.A literature review starts by retracing the introduction and rollout of computer vision algorithms in road environments, and goes on to demonstrate the importance of image descriptors in the processing chains implemented in such algorithms. It continues with a review of image descriptors from a novel approach, considering them in parallel with final applications, which opens up numerous analytical angles. Finally the literature review makes it possible to assess which descriptors are the most representative in road environments.Several databases containing images and associated meteorological data (e.g. rain, fog) are then presented. These databases are completely original because image acquisition and weather condition measurement are at the same location and the same time. Moreover, calibrated meteorological sensors are used. Each database contains different scenes (e.g. black and white target, pedestrian) and different kind of weather (i.e. rain, fog, daytime, night-time). Databases contain digitally simulated, artificial and natural weather conditions.Seven of the most representative image descriptors in road context are then selected and their robustness in rainy conditions is evaluated. Image descriptors based on pixel intensity and those that use vertical edges are sensitive to rainy conditions. Conversely, the Harris feature and features that combine different edge orientations remain robust for rainfall rates ranging in 0 – 30 mm/h. The robustness of image features in rainy conditions decreases as the rainfall rate increases. Finally, the image descriptors most sensitive to rain have potential for use in a camera-based rain classification application.The image descriptor behaviour in adverse weather conditions is not necessarily related to the associated final function one. Thus, two pedestrian detectors were assessed in degraded weather conditions (rain, fog, daytime, night-time). Night-time and fog are the conditions that have the greatest impact on pedestrian detection. The methodology developed and associated database could be reused to assess others final functions (e.g. vehicle detection, traffic sign detection).In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and streamlining travel. Currently, the only mean of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new method that uses road traffic cameras to detect weather conditions has therefore been proposed. This method uses a combination of a neural network and image descriptors applied to image patches. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and grades them according to their intensity. The method differentiates between normal daytime, rain, fog and normal night-time weather conditions. After several optimisation steps, the proposed method obtains better results than the ones reported in the literature for comparable algorithms.