Browsing by Author "Chamberlayne, Edward Pye"

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    A GIS Model for Minefield Area Prediction: The Minefield Likelihood Procedure
    Chamberlayne, Edward Pye (Virginia Tech, 2002-11-27)
    Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a "site suitability analysis" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the "high payoff" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data.
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    Optimal Evacuation Plans for Network Flows over Time Considering Congestion
    Chamberlayne, Edward Pye (Virginia Tech, 2011-06-09)
    This dissertation seeks to advance the modeling of network flows over time for the purposes of improving evacuation planning. The devastation created by Hurricanes Katrina and Rita along the Gulf Coast of the United States in 2005 have recently emphasized the need to improve evacuation modeling and planning. The lessons learned from these events, and similar past emergencies, have highlighted the problem of congestion on the interstate and freeways during an evacuation. The intent of this research is to develop evacuation demand management strategies that can reduce congestion, delay, and ultimately save lives during regional evacuations. The primary focus of this research will concern short-notice evacuations, such as hurricane evacuations, conducted by automobiles. Additionally, this dissertation addresses some traffic flow and optimization deficiencies concerning the modeling of congested network flows. This dissertation is a compilation of three manuscripts. Chapters 3 and 4 examine modeling network flows over time with congestion. Chapter 3 demonstrates the effects of congestion on flows using a microscopic traffic simulation software package, INTEGRATION. The flow reductions from the simulation are consistent with those found in several empirical studies. The simulation allows for the examination of the various contributing factors to the flow reductions caused by congestion, including level of demand, roadway geometry and capacity, vehicle dynamics, traffic stream composition, and lane changing behavior. Chapter 4 addresses some of the modeling and implementation issues encountered in evacuation planning and presents an improved modeling framework that reduces network flows due to congestion. The framework uses a cell-based linear traffic flow model within a mixed integer linear program (MILP) to model network flows over time in order to produce sets of decisions for use within an evacuation plan. The traffic flow model is an improvement based upon the Cell Transmission Model (CTM) introduced in Daganzo (1994) and Daganzo (1995) by reducing network flows due to congestion. The flow reductions are calibrated according to the traffic simulation studies conducted in Chapter 3. The MILP is based upon the linear program developed in Ziliaskopoulos (2000); however, it eliminates the "traffic holding" phenomenon where it cannot be implemented realistically within a transportation network. This phenomenon is commonly found in mathematical programs used for dynamic traffic assignment where the traffic is unrealistically held back in order to determine an optimum solution. Lastly, we propose additional constraints for the MILP that improve the computational performance by over 90%. These constraints exploit the relation of the binary variables based on the network topology. Chapter 5 applies the improved modeling framework developed in Chapter 4 to implement a demand management strategy called group-level staging -- the practice of evacuating different groups of evacuees at different times in order to reduce the evacuation duration. This chapter evaluates the benefits of group-level staging, as compared to the current practice of simultaneous evacuation, and explores the behavior of the modeling framework under various objective functions.