Anticipating changes to public transportation ridership demand is important to planning for and meeting service goals and maintaining system viability. These changes may occur in the short- or long-term; extensive academic work has focused on bettering long-term forecasting procedures while improvements to short-term forecasting techniques have not received significant academic attention. This dissertation combines traditional forecasting approaches with multivariate regression to develop a transferable short-term public transportation ridership forecasting model that incorporates fuel price as a prediction parameter. The research herein addresses 254 US transit systems from bus, light rail, heavy rail, and commuter rail modes, and uses complementary methods to account for seasonal and non-seasonal ridership fluctuations. Models were built and calibrated using monthly data from 2002 to 2007 and validated using a six-month dataset from early 2008. Using variable transformations, classical data decomposition techniques, multivariate regression, and a variety of forecasting model validation measures, this work establishes a benchmark for future research into transferable transit ridership forecasting model improvements that may aid public transportation system planners in an era when, due to fuel price concerns, global warming and green initiatives, and other impetuses, transit use is seeing a resurgence in popularity. / text

The 4-step process has been the standard procedure for transit forecasting for over 50 years. In recent decades, researchers have developed ridership forecasting regression models as alternatives to the costly and time consuming 4-step process. The model created by Lane, DiCarlantonio, and Usvyat in 2006 is among the most recent and most widely accepted. It includes station area demographics, central business district (CBD) employment, and the station areas’ built environments to estimate ridership. This report applies the Lane, DiCarlantonio, and Usvyat model to the North Line of Houston’s Metropolitan Transit Authority of Harris County (METRO). The report compares the 2030 ridership forecast created by METRO using the 4-step process with the LDU model forecasts. For the 2030 projections, this report obtained population and employment estimates from the Houston-Galveston Area Council and analyzed the data using Esri ArcMap and Caliper TRANSCadGIS software programs. The LDU model produced unrealistically high ridership numbers for the North Line. It estimated 108,430,481 daily boardings. METRO’s 4-step process predicted 29,900 daily boardings. The results suggest that the LDU model is not applicable to the Houston light rail system and is not a viable alternative to the 4-step process for this specific metropolitan area. The LDU method for defining Houston’s CBD was the main problem in applying the model. It calculated an extremely high CBD employment density compared to other cities of similar size. Even when the CBD size was manipulated to decrease employment density, the model still predicted 212,210 daily boardings for the North Line, nearly 10 times higher than METRO’s 4-step process estimate. In addition to the problems with the definition of the CBD, the creators of the LU model did not specifically explain how to define a metropolitan area. Multiple inconsistent and subjective definitions of a metro area can be used. This report employs three different definitions of the Houston metro, all of which produced three significantly different ridership forecasts in the LDU model. As a result of these flaws, the LDU model does not accurately apply to METRO’s North Line, and it does not serve as a viable alternative to METRO’s 4-step process. / text

The research presented herein concentrates on the quantification, assessment and forecasting of intra-hour wind power variability. Wind power is intrinsically variable and, due to the increase in wind power penetration levels, the level of intra-hour wind power variability is expected to increase as well. Existing metrics used in wind integration studies fail to efficiently capture intra-hour wind power variation. As a result, this can lead to an underestimation of intra-hour wind power variability with adverse effects on power systems, especially their reliability and economics. One major research focus in this dissertation is to develop a novel variability metric which can effectively quantify intra-hour wind power variability. The proposed metric, termed conditional range metric (CRM), quantifies wind power variability using the range of wind power output over a time period. The metric is termed conditional because the range of wind power output is conditioned on the time interval length k and on the wind power average production l[subscript j] over the given time interval. Using statistical analysis and optimization approaches, a computational algorithm to obtain a unique p[superscript th] quantile of the conditional range metric is given, turning the proposed conditional range metric into a probabilistic intra-hour wind power variability metric. The probabilistic conditional range metric CRM[subscript k,l subscript j,p] assists power system operators and wind farm owners in decision making under uncertainty, since decisions involving wind power variability can be made based on the willingness to accept a certain level of risk [alpha] = 1 - p. An extensive performance analysis of the conditional range metric on real-world wind power and wind speed data reveals how certain variables affect intra-hour wind power variability. Wind power variability over a time frame is found to increase with increasing time frame size and decreasing wind farm size, and is highest at mid production wind power levels. Moreover, wind turbines connected through converters to the grid exhibit lower wind power variability compared to same size simple induction generators, while wind power variability is also found to decrease slightly with increasing wind turbine size. These results can lead to improvements in existing or definitions of new wind power management techniques. Moreover, the comparison of the conditional range metric to the commonly used step-changes statistics reveals that, on average, the conditional range metric can accommodate intra-hour wind power variations for an additional 15% of hours within a given year, significantly benefiting power system reliability. The other major research focus in this dissertation is on providing intrahour wind power variability forecasts. Wind power variability forecasts use pth CRM quantiles estimates to construct probabilistic intervals within which future wind power output will lie, conditioned on the forecasted average wind power production. One static and two time-adaptive methods are used to obtain p[superscript th] CRM quantiles estimates. All methods produce quantile estimates of acceptable reliability, with average expected deviations from nominal proportions close to 1%. Wind power variability forecasts can serve as joint-chance constraints in stochastic optimization problems, which opens the door to numerous applications of the conditional range metric. A practical example application uses the conditional range metric to estimate the size of an energy storage system (ESS). Using a probabilistic forecast of wind power hourly averages and historical data on intra-hour wind power variability, the proposed methodology estimates the size of an ESS which minimizes deviations from the forecasted hourly average. The methodology is evaluated using real-world wind power data. When the estimated ESS capacities are compared to the ESS capacities obtained from the actual data, they exhibit coverage rates which are very close to the nominal ones, with an average absolute deviation less than 1.5%. / text

The Indigent Care Collaboration (ICC), a partnership of Austin, Texas, safety net providers, gathers encounter data and manages initiatives for the community's medically indigent patients. One such initiative is the establishment of a care management program designed to reduce avoidable hospitalizations. This study developed predictive models designed to take year-one encounter data and predict inpatient utilization in the following two years. The models were calibrated using 2003 through 2005 data for the 41,260 patients with encounters with ICC partner providers in all three years. Predictor variables included prior inpatient admissions, age, sex, and a summary measure of overall health status: the relative risk score produced by the Diagnostic Cost Groups prospective Medicaid risk-adjustment model. Using the 44,738 patients with encounter data in each of years 2004 through 2006 data, the performance of the predictive models was cross-validated and compared against the performance of the "common sense" method of choosing candidate patients based on prior year chronic disease diagnoses and high utilization, referred to herein as the Utilization Method (UM). The 620 patients with three or more 2005 through 2006 inpatient admissions were considered the actual high use patient subset. Each model's highest-risk 620 patients comprised its high-risk subset. Only 344 high-risk patients met the UM’s criteria. Prediction accuracy was described in terms of positive predictive value (PPV), i.e., the proportion of identified high-risk patients who were high-use patients. Three of the predictive models had a PPV of near 25% or greater, with the highest, the linear model using the DCG relative risk score, at 26.8%. The PPV of the UM was 17.1%, lower than that of all predictive models. When all high-risk subsets were limited to 344 patients (the number identified by the UM), the performance of the UM and the predictive models was similar. This study demonstrated that “common sense” targets for case management can be identified via simple filter as effectively as through empirically-based predictive models. However, once the supply of easily identifiable targets is exhausted, predictive models using a measure of health status identify high-risk patients who could not be easily identified by other means. / text

published_or_final_version / Housing Management / Master / Master of Housing Management

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

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