Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Ranked search on data graphs

Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity—users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. The purpose of this dissertation was to develop techniques for user-friendly, high quality and efficient searching of graph structured databases. Several ranked search methods on data graphs have been studied in the recent years. Given a top-k keyword search query on a graph and some ranking criteria, a keyword proximity search finds the top-k answers where each answer is a substructure of the graph containing all query keywords, which illustrates the relationship between the keyword present in the graph. We applied keyword proximity search on the web and the page graph of web documents to find top-k answers that satisfy user’s information need and increase user satisfaction. Another effective ranking mechanism applied on data graphs is the authority flow based ranking mechanism. Given a top- k keyword search query on a graph, an authority-flow based search finds the top-k answers where each answer is a node in the graph ranked according to its relevance and importance to the query. We developed techniques that improved the authority flow based search on data graphs by creating a framework to explain and reformulate them taking in to consideration user preferences and feedback. We also applied the proposed graph search techniques for Information Discovery over biological databases. Our algorithms were experimentally evaluated for performance and quality. The quality of our method was compared to current approaches by using user surveys.

A meta -analysis of empirical research studies on resilience among students at -risk for school failure

Being at-risk is a growing problem in the U.S. because of disturbing societal trends such as unemployment, divorce, substance abuse, child abuse and neglect, and the new threat of terrorist violence. Resilience characterizes individuals who rebound from or adapt to adversities such as these, and academic resilience distinguishes at-risk students who succeed in school despite hardships. ^ The purpose of this research was to perform a meta-analysis to examine the power of resilience and to suggest ways educators might improve academic resilience, which was operationalized by satisfactory test scores and grades. In order to find all studies that were relevant to academic resilience in at-risk kindergarten through 12th-grade students, extensive electronic and hardcopy searches were conducted, and these resulted in a database of 421 articles. Two hundred eighty seven of these were rejected quickly, because they were not empirical research. Upon further examination, another 106 were rejected for not meeting study protocol criteria. Ultimately, 28 studies were coded for study level descriptors and effect size variables. ^ Protective factors for resilience were found to originate in physical, psychological, and behavioral domains on proximal/intraindividual, transitional/intrafamilial, or distal/extrafamilial levels. Effect sizes (ESs) for these were weighted and the means for each level or category were interpreted by commonly accepted benchmarks. Mean effect sizes for proximal (M = .27) and for transitional (M = .15) were small but significant. The mean effect size for the distal level was insignificant. This supported the hypotheses that the proximal level was the source of most protective factors for academic resilience in at-risk students followed by the transitional level. The distal effect size warranted further research particularly in light of the small number of studies (n = 11) contributing effect sizes to that category. A homogeneity test indicated a search for moderators, i.e., study variables affecting outcomes, was justified. “Category” was the largest moderator. Graphs of weighted mean effect sizes in the physical, psychological, and behavioral domains were plotted for each level to better illustrate the findings of the meta-analysis. Suggestions were made for combining resilience development with aspects of positive psychology to promote resilience in the schools. ^