(Georgia Institute of Technology, 2017-03-28)
Wang, Jun
A new data envelopment analysis (DEA) based approach for benchmarking energy efficiency in buildings in the multifamily sector was proposed in this dissertation. It addressed major limitations of existing DEA model. It provides a method that remediates missing or incorrect values for instances in the dataset, establishes a mechanism that accurately and effectively detects outliers in the dataset, selects appropriate variables to be included in the DEA model and provides justifications for the selection, creates a DEA model that differently handles controllable and non-controllable variables, and quantitatively measures the stability of efficiency scores of each decision making units across the entire period. Data was provided by a third utility management and energy service company in the multifamily housing industry. Research deliverables are expected to provide decision makers and facility managers with the crucial information for building energy improvement. The limitations of future work are also discussed at the end of this dissertation.
(Georgia Institute of Technology, 2006-04-07)
Wang, Jun
Low speed urban streets are designed to provide both access and mobility, and accommodate multiple road users, such as bicyclists and pedestrians. However, speeds on these facilities often exceed the intended operating speeds as well as their design speeds. Several studies have indicated that the design speed concept, as implemented in the roadway design process in the United States, does not guarantee a consistent alignment that promotes uniform operating speeds less than design speeds. To overcome these apparent shortfalls of the design speed approach, a promising design approach is a performance-based design procedure with the incorporation of operating speeds. Under this procedure, the geometric parameters of the roadways are selected based on their influences on the desired operating speeds. However, this approach requires a clear understanding of the relationships between operating speeds and various road environments. Although numerous studies have developed operating speed models, most of these previous studies have concentrated on high speed rural two-lane highways. In contrast, highway designers and planners have very little information regarding the influence of low speed urban street environments on drivers' speeds.
This dissertation investigated the relationship between drivers' speed choices and their associated low speed urban roadway environments by analyzing second-by-second in-vehicle GPS data from over 200 randomly selected vehicles in the Atlanta, Georgia area. The author developed operating speed models for low speed urban street segments based on roadway alignment, cross-section characteristics, roadside features, and adjacent land uses. The author found the number of lanes per direction of travel had the most significant influence on drivers' speeds on urban streets. Other significant variables include on-street parking, sidewalk presence, roadside object density and offset, T-intersection and driveway density, raised curb, and adjacent land use. The results of this research effort can help highway designers and planners better understand expected operating speeds when they design and evaluate low speed urban roadways.