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 UNCERTAINTY ASSOCIATED WITH TRAVEL TIME PREDICTION: ADVANCED VOLATILITY APPROACHES AND ENSEMBLE METHODS(2015) Zhang, Yanru; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Travel time effectively measures freeway traffic conditions. Easy access to this information provides the potential to alleviate traffic congestion and to increase the reliability in road networks. Accurate travel time information through Advanced Traveler Information Systems (ATIS) can provide guidance for travelers' decisions on departure time, route, and mode choice, and reduce travelers' stress and anxiety. In addition, travel time information can be used to present the current or future traffic state in a network and provide assistance for transportation agencies in proactively developing Advanced Traffic Management System (ATMS) strategies. Despite its importance, it is still a challenging task to model and estimate travel time, as traffic often has irregular fluctuations. These fluctuations result from the interactions among different vehicle-driver combinations and exogenous factors such as traffic incidents, weather, demand, and roadway conditions. Travel time is especially sensitive to the exogenous factors when operating at or near the roadway's capacity, where congestion occurs. Small changes in traffic demand or the occurrence of an incident can greatly affect the travel time. As it is impossible to take into consideration every impact of these unpredictable exogenous factors in the modeling process, travel time prediction problem is often associated with uncertainty. This research uses innovative data mining approaches such as advanced statistical and machine learning algorithms to study uncertainty associated with travel time prediction. The final objective of this research is to develop more accurate and reliable travel time prediction models.Item Scalable Techniques for Behavioral Analysis and Forecasting(2011) Sliva, Amy; Subrahmanian, V.S.; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The ability to model, forecast, and analyze the behaviors of other agents has applications in many diverse contexts. For example, behavioral models can be used in multi-player games to forecast an opponent's next move, in economics to forecast a merger decision by a CEO, or in international politics to predict the behavior of a rival state or group. Such models can facilitate formulation of effective mitigating responses and provide a foundation for decision-support technologies. Behavioral modeling is a computationally challenging problem--real world data sets can contain on the order of 10^30,000 possible behaviors in any given situation. This work presents several scalable frameworks for modeling and forecasting agent behavior, particularly in the realm of international security dynamics. A probabilistic logic formalism for modeling and forecasting behavior is described, as well as distributed algorithms for efficient reasoning in this framework. To further cope with the scale of this problem, forecasting methods are also introduced that operate directly on time series data, rather than an intermediate behavioral model, to forecast actions and situations at some time in the future. Agent behavior can be adaptive, and in rare circumstances can deviate from the statistically "normal" past behavior. A system is also presented that can forecast when and how such behavioral changes will occur. These forecasting techniques, as well as any arbitrary time series forecasting approach, can be classified by a general axiomatic framework for forecasting in temporal databases. The knowledge gained from behavioral models and forecasts can be employed by decision-makers to develop effective response policies. An efficient framework is provided for identifying the optimal changes to the state of the world to elicit desired behaviors from another agent, balancing cost with likelihood of success. These modeling and analysis tools have also been incorporated into a prototype decision-support system and used in several case studies of real-world international security situations.