Browsing by Author "Dr. Peter Bloomfield, Committee Member"

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Adaptive Estimation and Prediction of Univariate Vehicular Traffic Condition Series
(2005-08-08) Guo, Jianhua; Dr. Joseph E. Hummer, Committee Member; Dr. Peter Bloomfield, Committee Member; Dr. Nagui M. Rouphail, Committee Member; Dr. Billy M. Williams, Committee Chair
Aimed at providing the anticipatory ability for the proactive traffic control systems, a new adaptive online short-term univariate traffic condition forecasting method is presented in this dissertation by assimilating knowledge from previous research. Using 15-minute traffic flow series as a representative, this methodology is based on the hypothesis that the first two conditional moments of univariate traffic flow series can be modeled as a SARIMA+GARCH structure, based on which an online forecasting system can be developed using seasonal exponential smoothing and Kalman filter. Supplementary components, including missing value imputation and outlier detection, can be incorporated into the system to meet the requirements of real traffic data collection situations. The development of the system follows two steps. In step (1), the SARIMA model is separated into a seasonal component handling traffic flow level due to historical traffic information, and a short-term component handling local variation after the traffic level due to historical information is subtracted. The seasonal component is processed using seasonal exponential smoothing by recognizing the theoretical equivalence between ARIMA model and exponential smoothing; the local variation is processed using Kalman filter by constructing a state space model. Afterwards, GARCH model is processed using Kalman filter based on the recognition that GARCH has an equivalent representation as ARMA in the sense of squared series. In step (2), missing values are replaced with one-step-ahead forecasts, and the system will not be updated since missing values convey no information. Outliers, indicating extraordinary patterns, are detected based on intervention analysis and likelihood ratio test. Outliers are proved to be assimilated into the system through an investigation showing outliers do have significant influences on the forecasting system. Additionally, the square root transformation is applied in the system. Using real traffic operation data from four regions, the research hypothesis is validated and the proposed methodology is implemented and proved to bear desirable attributes, including competencies, adaptability, computational efficiency, robustness, and transferability.
Bayesian Analysis of Circular Data Using Wrapped Distributions
(2003-01-27) Ravindran, Palanikumar; Dr. John Monahan, Committee Member; Dr. Sastry Pantula, Committee Member; Dr. Peter Bloomfield, Committee Member; Dr. Sujit K. Ghosh, Committee Chair
Circular data arise in a number of different areas such as geological, meteorological, biological and industrial sciences. We cannot use standard statistical techniques to model circular data, due to the circular geometry of the sample space. One of the common methods used to analyze such data is the wrapping approach. Using the wrapping approach, we assume that, by wrapping a probability distribution from the real line onto the circle, we obtain the probability distribution for circular data. This approach creates a vast class of probability distributions that are flexible to account for different features of circular data. However, the likelihood-based inference for such distributions can be very complicated and computationally intensive. The EM algorithm used to compute the MLE is feasible, but is computationally unsatisfactory. Instead, we use Markov Chain Monte Carlo (MCMC) methods with a data augmentation step, to overcome such computational difficulties. Given a probability distribution on the circle, we assume that the original distribution was distributed on the real line, and then wrapped onto the circle. If we can "unwrap" the distribution off the circle and obtain a distribution on the real line, then the standard statistical techniques for data on the real line can be used. Our proposed methods are flexible and computationally efficient to fit a wide class of wrapped distributions. Furthermore, we can easily compute the usual summary statistics. We present extensive simulation studies to validate the performance of our method. We apply our method to several real data sets and compare our results to parameter estimates available in the literature. We find that the Wrapped Double Exponential family produces robust parameter estimates with good frequentist coverage probability. We extend our method to the regression model. As an example, we analyze the association between ozone data and wind direction. A major contribution of this dissertation is to illustrate a technique to interpret the circular regression coefficients in terms of the linear regression model setup. Regression diagnostics can be developed after augmenting wrapping numbers to the circular data (refer Section 3.5). We extend our method to fit time-correlated data. We can compute other statistics such as circular autocorrelation functions and their standard errors very easily. We use the Wrapped Normal model to analyze the hourly wind directions, which is an example of the time series circular data.
Improvement of Photon Buildup Factors for Radiological Assessment
(2006-04-27) Schirmers, Fritz Gordon; Dr. Man-Sung Yim, Committee Chair; Dr. David McNelis, Committee Member; Dr. H. Omar Wooten, Committee Member; Dr. Donald Dudziak, Committee Member; Dr. Peter Bloomfield, Committee Member
Slant-path buildup factors for photons between 1 keV and 10 MeV for nine radiation shielding materials (air, aluminum, concrete, iron, lead, leaded glass, polyethylene, stainless steel, and water) are calculated with the most recent cross-section data available using Monte Carlo and discrete ordinates methods. Discrete ordinates calculations use a 244-group energy structure that is based on previous research at Los Alamos National Laboratory (LANL), but extended with the results of this thesis, and its focused studies on low-energy photon transport and the effects of group widths in multigroup calculations. Buildup factor calculations in discrete ordinates benefit from coupled photon/electron cross sections to account for secondary photon effects. Also, ambient dose equivalent (herein referred to as dose) buildup factors were analyzed at lower energies where corresponding response functions do not exist in literature. The results of these studies are directly applicable to radiation safety at LANL, where the dose modeling tool Pandemonium is used to estimate worker dose in plutonium handling facilities. Buildup factors determined in this thesis will be used to enhance the code's modeling capabilities, but should be of interest to the radiation shielding community.
Recursive Methods for Forecasting Short-term Traffic Flow Using Seasonal ARIMA Time Series Model.
(2004-12-31) Shekhar, Shashank; Dr. Billy M. Williams, Committee Chair; Dr. Peter Bloomfield, Committee Member; Dr. Nagui M. Rouphail, Committee Member
Many Intelligent Transportation System (ITS) applications under the umbrella of Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Services (ATIS) call for the ability to anticipate future traffic conditions. Short-term traffic forecasting models play a central role in such applications. Previous research has shown that a three parameter SARIMA time series model is well suited for forecasting short-term freeway traffic flow. However, past application has been in a static form where the model has to be fitted separately for each location. This research implements the seasonal ARIMA model in a time-varying format imparting plug and play capability to the model. The properties of the SARIMA model for short-term traffic flow forecasting are discussed. Model sensitivity to the parameters is shown. Three different methods (Kalman filter, recursive least squares filter and least mean square filter) have been investigated for making the model adaptive. The stability and robustness of the SARIMA model has been demonstrated. Results show that all the three adaptive filters can be successfully used to make the model adaptive. The use of Kalman filter for practical implementation is recommended. Recommendations for further research in this regard are also presented.
A Teaching Job Shop Control System with Real-time Inventory Management
(2005-07-08) Maxwell, Andrew Charles; Dr. Ola L.A. Harrysson, Committee Member; Dr. Peter Bloomfield, Committee Member; Dr. Robert E. Young, Committee Chair
This thesis presents a teaching job shop control system for running in assembly laboratories at colleges and universities in preparing Industrial Engineering students for challenges faced in real-world factories. Current techniques fail to encompass this idea of training for students like the proposed method does. Microsoft Access was used in creating a database that is the center point in this new system. Inventory is managed using this database system and added if parts are created in the manufacturing lab and moved to the assembly area. The system will stop if parts are low until new parts are created. In this new system, a pallet with an unfinished product on it moves down a conveyor system until it reaches the next workstation. At this station, the station operator scans a barcode on the pallet. This barcode contains what product is on this pallet. Based on this information, an ordered list of tasks appears on the workstation computer screen and must be done before the pallet can be moved on. When all tasks have been completed at a station, the station operator clicks 'done' on the screen and then can either move to the next pallet or end the run. Statistics are kept on the quality of the final products and parts as well as a work-in-process and on a goal percentage of good products out at the end of a one-hour time frame. Administrators will be able to assign tasks and parts to products and stations, as well as be able assign the goal ahead of time.
Travel time estimation from fixed point detector data
(2009-12-23) Yi, Ting; Dr. Peter Bloomfield, Committee Member; Dr. Nagui M.Rouphail, Committee Member; Dr. Billy M.Williams, Committee Chair; Dr. Joseph E. Hummer, Committee Member
YI, TING. Travel Time Estimation from Fixed Point Detector Data. (Under the direction of Dr. Billy M. Williams). Travel time, as a fundamental measurement for Intelligent Transportation Systems, is becoming increasingly important. Due to the wide deployment of the fixed point detectors on freeways, if travel time can be accurately estimated from point detector data, the indirect estimation method is cost-effective and widely applicable. This dissertation presents a systematic method for accurately estimating the travel time of different freeway links under various traffic conditions using fixed-point detector data. The proposed estimation system is based on a thorough analysis and comparison of the three categories of travel time estimation methods. The applications and limitations of each model are analyzed in terms of theory, equation derivation and possible modifications. Through a simulation study of various freeway links and traffic conditions, the various models have been compared according to performance measurements. The proposed systematic method is tested using both simulation data and real traffic data. A comparison of the estimated results and measurement errors shows the accuracy of the proposed systematic method for estimating the travel times of freeway links under various traffic conditions