Advancements in Global Positioning System (GPS) technology now make GPS data collection for transportation studies and other transportation applications a reality. Base map for the application can be obtained by importing the road centerline map into GIS software like AutoCAD Map, or Arc/Info or MapixTM. However, such kinds of Road Centerline maps are not available for all places. Therefore, it may be necessary to collect the data using GPS units. This thesis details the use of GPS technology to produce route maps that can be used to predict arrival time of a bus. This application is particularly useful in rural areas, since the bus headway in a rural area is generally larger than that in an urban area. The information is normally communicated through various interfaces such as internet, cable TV, etc., based on the GPS bus location data. The objective of this thesis is to develop an operational procedure to obtain the digitized route map of any desired interval or link length and to examine the accuracy of the digitized map. The operational procedure involved data collection, data processing, algorithm development and coding to produce the digitized route maps. An algorithm was developed produce the digitized route map from the base map of the route, coded in MATLAB, and can be used to digitize the base map into any desired interval of distance. The accuracy comparison is made to determine the consistency between the digitized route map and the base map. / Master of Science

The main purpose of this thesis is to combine modified GML algorithm with 2-D simulated annealing for estimation of signal arrival time in the UWB systems.In a dense multipath environment, the generalized maximum-likelihood (GML) algorithm can be used for the time-of-arrival (TOA) estimation. Nevertheless, the GML algorithm usually takes a long period of time, and sometimes fails to converge. Hence, a modified GML (MGML) algorithm is investigated. Two threshold parameters need to be determined in using the estimation algorithm. One threshold is to decide the arrival time range of estimated path, and the other, an amplitude threshold, is to judge whether the estimated path is true. Generally, the decision rule of thresholds may be based on the minimum error probability, which is defined as the sum of false alarm probability and miss probability. To mitigate the effects from noise and dense multipath interference, and to reduce the computational complexity of the algorithm, a method of threshold settings based on the minimum root mean square error (RMSE) criteria is discussed. In this scheme, the RMSE value for each candidate threshold pair in an appropriate region is computed. Constructing an accurate RMSE table and performing a full-scale grid search of adequate threshold settings can be very time-consuming. A 2-D simulated annealing process is adopted for finding the best pair of thresholds for use in the modified GML algorithm. The simulated annealing, different from the gradient descent, can avoid trapping into a local minimum in finding the best threshold pair. The resulting threshold pair makes the modified GML algorithm become more efficient in estimating the signal arrival time with an automatic search manner. Simulation results show that the proposed scheme can achieve better performance than the grid search approaches in UWB environments.

Public transportation operates in an environment which, due to its nature of numerous possibly influencing factors, is highly stochastic. This makes predictions of arrival times difficult, yet it’s important to be accurate in order to adhere to travelers expectations. In this study, the focus is on quantifying uncertainty around travel-time predictions as a means to improve the reliability of predictions in the context of public transportation. This is done by comparing Prediction Interval Coverage Probability (PICP) and Normalized Mean Prediction Interval Length (NMPIL). Three models, with two transformations of the response variable, were evaluated on real travel data from Skånetrafiken. The focus of the study was on examining a specific urban bus route, namely line 5 in Malmö, Sweden. The results indicated that a transformation based on the firstDifference achieved a better performance overall, but the results on a stopwise basis varied along the route. In terms of models, the uncertainty quantification revealed that Quantile Regression could be more appropriate at capturing data intervals which provide better coverage but at a shorter interval length, thus being more precise in its predictions. This is likely relatable to the robustness of the model and it being able to deal with extreme observations. A comparison with the current prediction model, which is agnostic in this study, revealed that the proposed point estimates from the Gaussian Process model based on the  firstDifference transformation outperformed the agnostic model on several stops. As such, further research is proposed as there is means for improvement in the current implementation.

Time resolved imaging is concerned with the measurement of photon arrival time. It has a wealth of emerging applications including biomedical uses such as fluorescence lifetime microscopy and positron emission tomography, as well as laser ranging and imaging in three dimensions. The impact of time resolved imaging on human life is significant: it can be used to identify cancerous cells in-vivo, how well new drugs may perform, or to guide a robot around a factory or hospital. Two essential building blocks of a time resolved imaging system are a photon detector capable of sensing single photons, and fast time resolvers that can measure the time of flight of light to picosecond resolution. In order to address these emerging applications, miniaturised, single-chip, integrated arrays of photon detectors and time resolvers must be developed with state of the art performance and low cost. The goal of this research is therefore the design, layout and verification of arrays of low noise Single Photon Avalanche Diodes (SPADs) together with high resolution Time-Digital Converters (TDCs) using an advanced silicon fabrication process. The research reported in this Thesis was carried out as part of the E.U. funded Megaframe FP6 Project. A 32x32 pixel, one million frames per second, time correlated imaging device has been designed, simulated and fabricated using a 130nm CMOS Imaging process from ST Microelectronics. The imager array has been implemented together with required support cells in order to transmit data off chip at high speed as well as providing a means of device control, test and calibration. The fabricated imaging device successfully demonstrates the research objectives. The Thesis presents details of design, simulation and characterisation results of the elements of the Megaframe device which were the author’s own work. Highlights of the results include the smallest and lowest noise SPAD devices yet published for this class of fabrication process and an imaging array capable of recording single photon arrivals every microsecond, with a minimum time resolution of fifty picoseconds and single bit linearity.

Waterflooding is currently the most commonly used method to improve oil recovery after primary depletion. The reservoir heterogeneity such as permeability distribution could negatively affect the performance of waterflooding. The presence of high permeability streaks could lead to an early water breakthrough at the producers and thus reduce the sweep efficiency in the field. One approach to counteract the impact of heterogeneity and to improve waterflood sweep efficiency is through optimal rate allocation to the injectors and producers. Through optimal rate control, we can manage the propagation of the flood front, delay water breakthrough at the producers and also increase the sweep and hence, the recovery efficiency. The arrival time optimization method uses a streamline-based method to calculate water arrival time sensitivities with respect to production and injection rates. It can also optimize sweep efficiency on multiple realizations to account for geological uncertainty. To extend the scope of this optimization method for more general conditions, this work utilized a finite difference simulator and streamline tracing software to conduct the optimization. Apart from sweep efficiency, another most widely used optimization method is to maximize the net present value (NPV) within a given time period. Previous efforts on optimization of waterflooding used optimal control theorem to allocate injection/production rates for fixed well configurations. The streamline-based approach gives the optimization result in a much more computationally efficient manner. In the present study, we compare the arrival time optimization and NPV optimization results to show their strengths and limitations. The NPV optimization uses a perturbation method to calculate the gradients. The comparison is conducted on a 4- spot synthetic case. Then we introduce the accelerated arrival time optimization which has an acceleration term in the objective function to speed up the oil production in the field. The proposed new approach has the advantage of considering both the sweep efficiency and net present value in the field.

Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.

Estimating the quantity of water that reaches the water table following an infiltration event is vital for modeling and management of water resources. Estimating the time scale of groundwater recharge after a rainfall event is difficult because of the dependence on nonlinear soil characteristics and variability in antecedent conditions. Modeling the flow of water through the variably saturated zone is computationally intensive since it requires simulation of Richards' equation, a nonlinear partial differential equation without a closed-form analytical solution, with parametric relationships that are difficult to approximate. Hence, regional scale coupled (surface water - groundwater) hydrological models make simplistic assumptions about the quantity and timing of recharge following infiltration. For simplicity, such models assume the quantity of recharge to be a fraction of the total rainfall and the time to recharge the saturated groundwater is scaled proportionally to the depth to water table, in lieu of simulating computationally intensive flow in the variably saturated zone. In integrated or coupled (surface water - groundwater) regional scale hydrological models, better representation of the timing and quantity of groundwater recharge is required and important for water resources management. This dissertation presents a practical groundwater recharge estimation method and relationships that predict the timing and volume accumulation of groundwater recharge to moderate to deep water table settings. This study combines theoretical, empirical, and simulation techniques to develop a relatively simple model to estimate the propagation of the soil moisture wetting front through variably saturated soil. This model estimates the time scale and progression of recharge following infiltration for a specified depth to water table, saturated hydraulic conductivity and equilibrium moisture condition. High-resolution soil moisture data from a set of experiments conducted in a laboratory soil column were used to calibrate the HYDRUS-1D model. The calibrated model was used to analyze the time scale of recharge by varying soil hydraulic properties and simulating the application of rainfall pulses of varying volume and intensities. Modeling results were used to develop an equation that relates the non-dimensional travel time of the wetting front to excess moisture moisture content above equilibrium. This research indicates that for a soil with a known retention curve, the wetting front arrival time at a given depth can be described by a power law, where the power is a function of the saturated hydraulic conductivity. This equation relates the non-dimensional travel time of the wetting front to excess moisture content above the equilibrium moisture content. Since the equilibrium moisture content is dependent on the water retention curve, the powers in the equation governing the timing of recharge depend on the saturated hydraulic conductivity for a large variation in water retention curve. Also, the power law relates recharge (normalized by applied pulse volume) to time (normalized by the time of arrival of wetting front at that depth). The resulting equations predicted the model simulated normalized (relative) recharge with root mean square errors of less than 14 percent for the tested cases.

Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus’s current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.

Orientadores: Milton Mori, Henry França Meier, Udo Fritsching / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica / Made available in DSpace on 2018-08-10T10:03:01Z (GMT). No. of bitstreams: 1 Decker_RodrigoKoerich_D.pdf: 3679156 bytes, checksum: 9c914896fd9d31753adf9c1bed158cac (MD5) Previous issue date: 2008 / Resumo: Este trabalho propõe o estudo de estruturas coerentes de larga escala por meio da utilização da metodologia ¿Interparticle Arrival Time¿ (IAT) no escoamento de um jato bifásico. Experimentos foram desenvolvidos em relação a diferentes condições de velocidade inicial com diâmetro médio de partícula igual a 50 µm, e para duas diferentes misturas de partículas, 50 µm e 90 µm, em diferentes proporções, e comparados em relação a perfis de velocidade média, intensidade de turbulência e velocidade RMS. Medidas relacionadas à distribuição IAT foram também adquiridas para todas as condições analisadas. Os experimentos foram desenvolvidos para diferentes posições axiais e radiais a partir da saída do orifício de formação do jato. Perfis radiais de velocidade média, flutuação de velocidade (velocidade RMS), intensidade de turbulência e ¿interparticle arrival time¿ (em termos de distribuição Chi2 e número de desvios) foram obtidos pelo sistema de ¿Phase Doppler Anemometry¿, atravessando o sistema de medição ponto a ponto na direção desejada. Além disto, as variações das condições de velocidade inicial, distribuição de partículas e razão de carga permitem a obtenção de importantes informações em relação às estruturas locais do escoamento e seus efeitos sobre o transporte macroscópico e turbulento de partículas entre o centro do jato e as regiões de contorno do mesmo. Assim é possível identificar que no centro do jato não existe a formação de Estruturas Coerentes de Larga Escala (ECLE), ou seja, o escoamento é dominado por estruturas incoerentes. Existem também fortes indícios de formação de ECLE em uma região radial entre o centro e a região de contorno, sendo estas dependentes da condição inicial de velocidade da fase gás. Além disto, partículas com maior diâmetro suprimem a formação de ECLE. A distribuição IAT prova ser uma ferramenta importante na identificação dos locais onde ECLEs vêm a influenciar a distribuição de partículas, formando ¿clusters¿. A investigação extensiva de dados experimentais em relação ao comportamento da fase dispersa no escoamento gás sólido em um jato pode ser utilizada como uma importante fonte de dados para uma validação detalhada, por meio de simulação numérica, do escoamento disperso bifásico, incluindo as fortes interações entre as fases gás e particulada / Abstract: A study of large scale coherent structures by Interparticle Arrival Time (IAT) of a two phase jet flow is proposed. Measurements were carried out for different initial velocities with 50 µm particle mean diameter, and for two different particle mixtures with mean particle diameter of 50 µm and 90 µm, in different proportions, and analyzed in relation to different variables. Measurements related to IAT were also acquired for all analysis conditions. The experiments were developed for different axial and radial distances from the nozzle outlet. Radial profiles of mean velocity, RMS velocity, turbulence intensity and the IAT (in terms of Chi2 and number of deviation) were measured by a Phase Doppler Anemometry system, traversing the measuring device stepwise in the desired direction. Furthermore, the variation of the initial velocity conditions, particle diameter distributions, and particle loadings yield important information about the local flow structures and its effect on the macroscopic as well as the turbulent particle transport between the jet centre and the outer shear layer. Thus, it is possible to identify that in the centre line of the jet there is no formation of large scale coherent structures (ECLE), i. e., the flow is dominated by incoherent structures. There is also strong evidence of ECLE formation in a radial position between the centre and the outer shear layer of the jet, which are dependent on the gas initial velocity. Furthermore, particles with large diameter suppress ECLE formation. The IAT distribution proofs to be an important tool to identify regions where large scale coherent structures influence the particle distribution and tend to form particle clusters. The derived extensive experimental data set for the particle behaviour at the two-phase jet may serve as a basis for the detailed validation of numerical simulations of dispersed two phase flow behaviour including strong phase interactions between gaseous and particulate phases / Doutorado / Desenvolvimento de Processos Químicos / Doutor em Engenharia Química

Moderne Experimente in der Kurzzeitphysik erfordern eine hochpräzise Synchronisation der beteiligten Strahlungsquellen, um dynamische Prozesse und atomare Strukturen aufzulösen. Die Komplexität und räumliche Ausdehnung einer linearbeschleuniger-getriebenen Strahlungsquelle wie ELBE verlangt nach neuen Konzepten, um die anspruchsvollen Anforderungen zu erfüllen. Kernbestandteile der vorliegenden Arbeit sind die Konzeption, der Aufbau und die Inbetriebnahme eines gepulsten optischen Synchronisationssystems zur Verteilung eines Phasenreferenzsignals. Dieses System bildet eine wesentliche Grundvoraussetzung für wissenschaftliche Experimente mit einer Auflösung im Bereich von wenigen zehn Femtosekunden. Darüber hinaus wurde der Prototyp eines Ankunftszeitmonitors am ELBE-Beschleuniger entwickelt und charakterisiert. Mit diesem Diagnoseelement wurden erstmals Messungen der Elektronenpulsankunftszeit mit einer Auflösung von wenigen Femtosekunden am ELBE-Strahl möglich. Die implementierte Datenanalyse erlaubt einzelpulsaufgelöste Messzyklen mit bisher unerreichter spektraler Bandbreite am kontinuierlichen (CW) Elektronenstrahl. Ferner wurde eine Methode zur Datenerfassung entwickelt, die unter Verwendung der Lockin-Technik besonders rauscharme Messungen hervorbringen kann. Abschließend wurde der ELBE-Beschleuniger hinsichtlich Ankunftszeit und Energiestabilität umfassend untersucht. Dabei wurden die erweiterten Möglichkeiten, die ELBE als CW-Beschleuniger bietet, ausgeschöpft. Der Fokus lag besonders auf der spektralen Analyse der Störungen bei verschiedenen Kompressionszuständen der Elektronenpulse. Diese methodische Untersuchung wurde sowohl für den thermionischen Injektor als auch für die supraleitende Fotoelektronenquelle durchgeführt. Die präsentierten Messergebnisse ermöglichen ein erweitertes Verständnis für die wirkenden Störmechanismen während der Elektronenpulspropagation und stellen den Ausgangspunkt für systematische Verbesserungen der Strahlqualität dar. Ein beschriebener Grundlagenversuch belegt, wie der ELBE-Elektronenstrahl in Zukunft aktiv stabilisiert werden kann, um die erforderliche Zeitauflösung zu erreichen. Intrinsische Strahlinstabilitäten können dadurch signifikant reduziert werden.