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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    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.
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    Profile Based Topology Control and Routing in Wireless Optical Networks
    (2004-05-04) Kashyap, Abhishek; Shayman, Mark A; Electrical Engineering
    The problem of topology control and routing of bandwidth-guaranteed flows over wireless optical backbone networks is addressed. The input is a potential topology and a traffic profile. The constraints are that of limited interfaces at each node and the limited link bandwidth, and the objective is to maximize the throughput. The problem turns out to be NP-Hard. A new framework for integrated topology control and routing is proposed. A simple heuristic is proposed, and efficient rollout algorithms are proposed which enhance the heuristic. The routing problem is formulated as a multi-commodity flow problem, and is used to enhance the rollout algorithms to achieve a higher throughput. Another set of heuristics is proposed which use matching theory and multi-commodity flow formulation of routing to achieve the desired results. We enhance the heuristics to provide fairness to the ingress-egress pairs in terms of how much traffic we route for each of them.