UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item An Expectation Maximization Approach to Revenue Management on Rail Ticket Data(2016) Kaushik, Kartik; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the world of sale of perishable commodities without regulation, competition causes cut-throat pricing and heavy discounts for the commodity. Even though this is beneficial to the customers, the companies that offer the commodity have to be careful to prevent the offered discounts and cut-throat pricing from cutting into their profits. The science of managing revenues in such scenarios is loosely termed as Revenue Management (RM). RM holds its roots to the competition generated in the American airline industry after deregulation. Since then, it has spread to virtually all industries that deal with perishable commodities such as hotel and hospitality, rental vehicles, and all forms of long distance public transportation, even freight. The commodities in these industries refer to the items for sale. In a hotel, it may be rooms of different classes and sizes; in vehicle rentals, cars; and in all forms of long distance transportation, seating space. Perishability of these commodities can be understood simply by the fact that after a certain date, a certain commodity will not be available. In long distance transportation, it is easy to imagine that the seats on a vehicle (plane, bus, train or ferry) will not be available after the vehicle has departed on its way. Similarly rooms in a hotel or cars with a rental agency will loose value the longer they are kept empty or unused. The goal of modern day RM is, therefore, to ensure profitable sales of such commodities, such that they are priced at better rates than the competition. This thesis attempts to apply the theory of Expectation Maximization (EM) to the purchase data from railway industry in a attempt to better the existing pricing logic. The EM algorithm used here was developed by Dr. Kalyan Talluri and Dr. Gareth van Ryzin in their seminal paper published in 2004. In that paper the authors develop the algorithm, derive the mathematics that powers it and apply it to test data sets to prove that it out performs the current industry standard. However, application of that method to a real dataset has never been done, which is the goal of this thesis. We find, and document herewith, the issues that resulted from applying the EM algorithm directly to the data. Mainly, assumptions in the EM algorithm required heavy data clean up, after which it was found that the results were neither satisfactory nor useful. The reasons for the failure of the model are examined in detail, the primary reason being lack of identifiability in the data. To conclude, the EM algorithm needs substantial modification or additional data in order to lose certain debilitating assumptions and make it more general or reduce the identifiability problem of the data.Item Estimation of Mixed Distributions on Vehicular Traffic Measurements using the Bluetooth Technology(2012) Zoto, Jorgos; La, Richard; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this work we build on the idea of using Bluetooth® sensors as a new intelligent transportation system application of estimating travel time along a section of a highway. Given the existence of High Occupancy Vehicle (HOV) lanes and Express lanes in the U.S highway network, a mixed population estimation problem naturally arises. This estimation problem is attacked from three dierent perspectives: (i) in light of the Expectation Maximization (EM) algorithm, (ii) using Maximum Likelihood Estimation (MLE) techniques and nally (iii) applying a cluster-separation approach to our mixed dataset. The robust performance of the rst approach leads to an EM-inspired MLE technique, a hybrid of (i) and (ii) which combines the good estimation accuracy of EM based algorithms and the lower complexity of MLE techniques. The limitations and performance of all four approaches are tested on actual vehicular data on different highway segments in two dierent U.S states. The superiority of the hybrid approach is shown along with it's limitations.Item Gaussian Process Regression for Model Estimation(2008) Srinivasan, Balaji Vasan; Duraiswami, Ramani; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)State estimation techniques using Kalman filter and Particle filters are used in a number of applications like tracking, econometrics, weather data assimilation, etc. These techniques aim at estimating the state of the system using the system characteristics. System characteristics include the definition of system's dynamical model and the observation model. While the Kalman filter uses these models explicitly, particle filter based estimation techniques use these models as part of sampling and assigning weights to the particles. If the state transition and observation models are not available, an approximate model is used based on the knowledge of the system. However, if the system is a total black box, it is possible that the approximate models are not the correct representation of the system and hence will lead to poor estimation. This thesis proposes a method to deal with such situations by estimating the models and the states simultaneously. The thesis concentrates on estimating the system's dynamical model and the states, given the observation model and the noisy observations. A Gaussian process regression based method is developed for estimating the model. The regression method is sped up from O(N2) to O(N) using an data-dependent online approach for fast Gaussian summations. A relevance vector machine based data selection scheme is used to propagate the model over iterations. The proposed method is tested on a Local Ensemble Kalman Filter based estimation for the highly non-linear Lorenz-96 model.