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No description available.

No description available.

No description available.

No description available.

The potential real-time control (RTC) has to improve the performance of existing stormwater management systems is a topic of increasing interest as hydraulic and hydrologic modeling capabilities proliferate. The benefits of incorporating precipitation forecast data into a RTC algorithm to allow for prediction-based control of an urban watershed is explored using an EPA SWMM 5.1 watershed model. One reactive and two predictive RTC algorithms are simulated in various configurations across seven dry detention ponds located in the 162 hectare urbanized watershed. The hydraulic benefits they provide at the site and watershed outlet in regards to peak flow and the flow duration curve are compared to conventional, static control. The ponds retrofit with the novel predictive RTC algorithm had lower peak flows during 24-hour design storms more consistently than when retrofit with reactive RTC. The duration of erosive flows at the site level was decreased by the novel predictive RTC in most cases. Improvements at the watershed outlet depended on where RTC was applied as hydrograph compounding was observed during some RTC implementations. / Master of Science / The consequences of watershed urbanization on nearby waterways has become a more relevant concern as urbanization increases and climate change continues to develop. Conventional stormwater management practices are employed to control peak flows from urbanized drainage areas for certain design storm criteria. Real-time control (RTC) technology has the potential to enable existing stormwater facilities to improve their performance during storm events different from their design conditions. This study compares the performance of several reactive and predictive rule-based RTC algorithms simulated as retrofits on seven dry detention ponds in a 162 hectare urbanized watershed. The results indicate that RTC algorithms that use rainfall forecast data for predictive decision making have the most potential to reduce stream erosion when applied appropriately throughout the watershed.

The purpose of this study was to estimate the drivers' travel time with the occurrence of an incident on freeway. Three approaches, which were shock wave analysis, queuing theory and cellular automata models, were initially considered, however, the first two macroscopic models were indicated to underestimate travel time by previous literature. A microscopic simulation model based on cellular automata was developed to attain the goal. The model incorporated driving behaviors on the freeway with the presence of on-ramps, off-ramps, shoulder lanes, bottlenecks and incidents. The study area was a 16 mile eastbound section of I-66 between US-29 and I-495 in northern Virginia. The data for this study included loop detector data and incident data for the road segment for the year 2007. Flow and speed data from the detectors were used for calibration using quantitative and qualitative techniques. The cellular automata model properly reproduced the traffic flow under normal conditions and incidents. The travel time information was easily obtained from the model. The system is promising for travel time estimation in near real time. / Master of Science

One of the ways to define mobility in a transportation system is total travel time for all travelers using the transportation network. A good assessment of the mobility is essential for knowing the points of congestion in the network and the factors responsible for the congestion. Also the change in mobility from the baseline to the horizon year would give the modeler an idea of the effectiveness of the various transportation systems. One of the applications of the mobility measurement is the evaluation of aviation technologies proposed by FAA to ease the congestion. This paper addresses a method to estimate the mobility of the air transportation network in the baseline year (2000). Also presented is a method to estimate the mobility to the horizon year by considering congestion on the roadway. / Master of Science

Currently, there is a large body of research examining Out-of-School Time (OST) programs, the goals of these programs, and their reported impact on the youth they serve. However, there is little evidence of research on how organizations determine which goals best fit the needs of the communities they serve. Concurrently, studies of how organizations put these goals into action are virtually non-existent in the literature. This study examines how organizations in Massachusetts receiving federal 21st Century Community Learning Center funding make decisions about Out-of-School Time programming. Although the federal grant requirements and the Massachusetts Department of Education impose some uniformity on these programs, they still vary considerably in their design and the target audiences they serve. Through an online survey, document review and semi-structured interviews, the research identified several themes about the decision making used in these organizations. Cohen, March & Olson's Garbage Can Model of Organizational Choice was used as a framework to better understand the data and to organize the discussion. A stakeholder analysis was also used to evaluate the influence of participants on the decision making within the Out-of-School Time programs. The research identified a loose connection between the decisions made about the types of activities selected and the prescriptive goals of the 21st Century Community Learning Centers programs. There were several factors that contributed to this loose connection, including the influence of stakeholders, the learning outcomes identified by the assessment tools, and the evaluation of programs by the state. The research also leads to several recommendations about the evaluation of these programs in Massachusetts, including a review of the assessment tools designed by the state and a study of whether the mission shift evidenced in these communities is a tacit decision made by the Department of Education and grant recipients. Further research on the alignment of federal and state programming goals is also recommended.

Forecasting tools play an important role in public response to epidemics. Despite this, limited work has been done in comparing best-in-class techniques across the broad spectrum of time series forecasting methodologies. Forecasting frameworks were developed that utilised three methods designed to work with nonlinear dynamics: Iterated Filtering (IF) 2, Hamiltonian MCMC (HMC), and S-mapping. These were compared in several forecasting scenarios including a seasonal epidemic and a spatiotemporal epidemic. IF2 combined with parametric bootstrapping produced superior predictions in all scenarios. S-mapping combined with Dewdrop Regression produced forecasts slightly less-accurate than IF2 and HMC, but demonstrated vastly reduced running times. Hence, S-mapping with or without Dewdrop Regression should be used to glean initial insight into future epidemic behaviour, while IF2 and parametric bootstrapping should be used to refine forecast estimates in time. / Thesis / Master of Science (MSc)