Theses and Dissertations from UMD
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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 give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item ONLINE and REAL-TIME TRANSPORTATION SYSTEMS MANAGEMENT and OPERATIONS DECISION SUPPORT WITH INTEGRATED TRAVEL BEHAVIOR and DYNAMIC NETWORK MODELS(2018) Zhu, Zheng; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The acceleration of urbanization is witnessed all around the world. Both population and vehicle ownership are rapidly growing, and the induced traffic congestion becomes an increasingly pervasive problem in people’s daily life. In 2014, transportation congestion caused $160 billion economic loss in 498 U.S. urban areas, which is 5.5 more than that in 1982. Without effective reactions, this number is expect to grow to $192 billion in 2020. In order to mitigate traffic congestion, many transportation demand management (TDM) strategies (e.g. bus rapid lanes, and flextime policy), and active traffic management (ATM) strategies (e.g. real-time user guidance, and adaptive traffic signal control) have been proposed and implemented. Although TDM and ATM have proved their values in theoretical researches or field implementations, it is still hard for transportation engineers to select the optimal strategy when faced with complex traffic conditions. In the science of transportation engineering, mathematical models are usually expected to help estimate traffic conditions under different scenarios. There have been a number of models that help transportation engineers make decisions. However, many of them are developed for offline use and are not suitable for real-time applications due to computational time issues. With the development of computational technologies and traffic monitoring systems, online transportation network modeling is getting closer and closer to reality. The objective of this dissertation is to develop a large-scale mesoscopic transportation model which is integrated with an agent-based travel behavior model. The ultimate goal is to achieve online (real-time) simulation to estimate and predict the traffic performance of the entire Washington D.C. area. The simulation system is expected to support real-time transportation system managements and operations. One of the most challenging issue for this dissertation is the calibration of online simulation models. Model parameters need to be estimated based on real-time traffic data to reflect the reality. Literature review of previous relevant studies indicates a trade-off between computational speed and calibration accuracy. In order to apply the model onto a real-time horizon, experts usually ignore the inherent mechanism of traffic modeling but rely on fast converging technologies to approximate the model parameters. Differently from previous online transportation simulation approaches, the method proposed in this dissertation focuses more on the mechanism of transportation modeling. With the fundamental understanding of the modeling mechanism, one can quickly determine the gradient of model parameters such that the gap between real-time traffic measures and simulation results is minimized. This research is one of the earliest attempts to introduce both agent-based modeling and gradient-based calibration approach to model real-time large-scale networks. The contribution includes: 1) integrate an agent-based travel behavior model into dynamic transportation network models to enhance the behavior realism; 2) propose a fast online calibration procedure that quickly adjusts model parameters based on real-time traffic data. A number of real-world case studies are illustrated to demonstrate the value of this model for both long-term and real-time applications.Item Online Decision Making via Prophet Setting(2017) Ehsani Banafati, Soheil; Hajiaghayi, MohammadTaghi; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the study of online problems, it is often assumed that there exists an adversary who acts against the algorithm and generates the most challenging input for it. This worst-case assumption in addition to the complete uncertainty about future events in the traditional online setting sometimes leads to worst-case scenarios with super-constant approximation impossibilities. In this dissertation, we go beyond this worst-case analysis of problems by taking advantage of stochastic modeling. Inspired by the prophet inequality problem, we introduce the prophet setting for online problems in which the probability distributions of the future inputs are available. This modeling not only considers the availability of statistical data in the design of mechanisms but also results in significantly more efficient algorithms. To illustrate the improvements achieved by this setting, we study online problems within the contexts of auctions and networks. We begin our study with analyzing a fundamental online problem in optimal stopping theory, namely prophet inequality, in the special cases of iid and large markets, and general cases of matroids and combinatorial auctions and discuss its applications in mechanism design. The stochastic model introduced by this problem has received a lot of attention recently in modeling other real-life scenarios, such as online advertisement, because of the growing ability to fit distributions for user demands. We apply this model to network design problems with a wide range of applications from social networks to power grids and communication networks. In this dissertation, we give efficient algorithms for fundamental network design problems in the prophet setting and present a general framework that demonstrates how to develop algorithms for other problems in this setting.Item A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications(2012) Won, Stephen; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In networked signal processing systems, dataflow graphs can be used to describe the processing on individual network nodes. However, to analyze the correctness and performance of these systems, designers must understand the interactions across these individual "node-level'' dataflow graphs --- as they communicate across the network --- in addition to the characteristics of the individual graphs. In this thesis, we present a novel simulation environment, called the NS-2 -- TDIF SIMulation environment (NT-SIM). NT-SIM provides integrated co-simulation of networked systems and combines the network analysis capabilities provided by the Network Simulator (ns) with the scheduling capabilities of a dataflow-based framework, thereby providing novel features for more comprehensive simulation of networked signal processing systems. Through a novel integration of advanced tools for network and dataflow graph simulation, our NT-SIM environment allows comprehensive simulation and analysis of networked systems. We present two case studies that concretely demonstrate the utility of NT-SIM in the contexts of a heterogeneous signal processing and data mining system design.Item DYNAMIC DECISION MAKING FOR LESS-THAN-TRUCKLOAD TRUCKING OPERATIONS(2009) Hejazi, Behrang; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)On a typical day, more than 53 million tons of goods valued at about $36 million are moved on the US multimodal transportation network. An efficient freight transportation industry is the key in facilitating the required movement of raw materials and finished products. Among different modes of transportation, trucking remains the shipping choice for many businesses and is increasing its market share. Less-than-truckload (LTL) trucking companies provide a transportation service in which several customers are served simultaneously by using the same truck and shipments need to be consolidated at some terminals to build economical loads. Intelligent transportation system (ITS) technologies increase the flow of available data, and offer opportunities to control the transportation operations in real-time. Some research efforts have considered real-time acceptance/rejection of shipping requests, but they are mostly focused on truckload trucking operations. This study tries to use real-time information in decision making for LTL carriers in a dynamically changing environment. The dissertation begins with an introduction of LTL trucking operations and different levels of planning for this type of motor carriers, followed by the review of literature that are related to tactical and operational planning. Following a brief discussion on multi commodity network flow problems and their solution algorithm, a mathematical model is proposed to deal with the combined shipment and routing problem. Furthermore, a decision making procedure as well as a decision support application are developed and are presented in this dissertation. The main step in the decision making procedure is to solve the proposed mathematical problem. Three heuristic solution algorithms are proposed and the quality of the solutions is evaluated using a set of benchmark solutions. Three levels of numerical experiments are conducted considering an auto carrier that operates on a hub-and-spoke network. The accuracy of the mathematical model and the behavior of the system under different demand/supply situations are examined. Also, the performance of the solutions provided by the proposed heuristic algorithms is compared and the best solution method is selected. The study suggests that significant reductions in operational costs are expected as the result of using the proposed decision making procedure.