Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

The development and testing of a multivariate model of differentiation of self among Hispanics

Individuals of Hispanic origin are the nation's largest minority (13.4%). Therefore, there is a need for models and methods that are culturally appropriate for mental health research with this burgeoning population. This is an especially salient issue when applying family systems theories to Hispanics, who are heavily influenced by family bonds in a way that appears to be different from the more individualistic non-Hispanic White culture. Bowen asserted that his family systems' concept of differentiation of self, which values both individuality and connectedness, could be universally applied. However, there is a paucity of research systematically assessing the applicability of the differentiation of self construct in ethnic minority populations. ^ This dissertation tested a multivariate model of differentiation of self with a Hispanic sample. The manner in which the construct of differentiation of self was being assessed and how accurately it represented this particular ethnic minority group's functioning was examined. Additionally, the proposed model included key contextual variables (e.g., anxiety, relationship satisfaction, attachment and acculturation related variables) which have been shown to be related to the differentiation process. ^ The results from structural equation modeling (SEM) analyses confirmed and extended previous research, and helped to illuminate the complex relationships between key factors that need to be considered in order to better understand individuals with this cultural background. Overall results indicated that the manner in which Hispanic individuals negotiate the boundaries of interconnectedness with a sense of individual expression appears to be different from their non-Hispanic White counterparts in some important ways. These findings illustrate the need for research on Hispanic individuals that provides a more culturally sensitive framework. ^

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

The efficacy of the Ellison model as a retention initiative for first semester freshmen

The freshman year is the most critical year of matriculation for students in higher education. One in four freshman students drops out of higher education after the first year. In fact, the first two to six weeks of college represent a very critical transition period when students make the decision to persist or depart from the institution. Many students leave because they are unable to make a connection with the institution. Retention is often profoundly affected by student involvement in the academic environment, satisfaction with the campus climate and the institution's response to diversity. Therefore, the purpose of this study was to examine and evaluate an effective institutional response that promotes freshman retention and academic success. The tenets (diversity training, conflict management, and community building) of a mentoring model were applied to the freshman experience seminar class (experimental group) as a pedagogical method of instruction to determine its efficacy as a retention initiative when compared with the traditional freshman experience seminar class (comparison group). ^ The quantitative study employed a quasi-experimental research design based on Astin's (1993) I-E-O model. The model examined the relationships between the characteristics students bring with them to college, called inputs, their experiences in the environment during college, and the outcomes students achieved during matriculation. Fifty-two students enrolled in the freshman seminar class participated in the study. ^ Demographic data and input variables between groups were analyzed using chi-square, t-tests and multivariate analyses. Overall, students in the experimental group had significantly higher satisfaction (campus climate) scores than the comparison group. An analysis of the students' willingness to interact with others from diverse groups indicated a significant difference between groups, with the experimental group scoring higher than the comparison group. Students in the experimental group were significantly more involved in campus activities than students in the comparison group. No significant differences were found between groups relative to the mean grade point average and re-enrollment for fall semester 2001. ^ While the mentoring model did not directly affect re-enrollment of students, the model did promote student satisfaction with the institution, an appreciation for diversity of contact and it encouraged involvement in the campus community. These are all essential outcomes of a quality retention program. ^

Variables affecting the success of older applicants for employment in a public organization: An age -cued cognitive model of administrative decision-making

The dissertation takes a multivariate approach to answer the question of how applicant age, after controlling for other variables, affects employment success in a public organization. In addition to applicant age, there are five other categories of variables examined: organization/applicant variables describing the relationship of the applicant to the organization; organization/position variables describing the target position as it relates to the organization; episodic variables such as applicant age relative to the ages of competing applicants; economic variables relating to the salary needs of older applicants; and cognitive variables that may affect the decision maker's evaluation of the applicant. ^ An exploratory phase of research employs archival data from approximately 500 decisions made in the past three years to hire or promote applicants for positions in one public health administration organization. A logit regression model is employed to examine the probability that the variables modify the effect of applicant age on employment success. A confirmatory phase of the dissertation is a controlled experiment in which hiring decision makers from the same public organization perform a simulated hiring decision exercise to evaluate hypothetical applicants of similar qualifications but of different ages. The responses of the decision makers to a series of bipolar adjective scales add support to the cognitive component of the theoretical model of the hiring decision. A final section contains information gathered from interviews with key informants. ^ Applicant age has tended to have a curvilinear relationship with employment success. For some positions, the mean age of the applicants most likely to succeed varies with the values of the five groups of moderating variables. The research contributes not only to the practice of public personnel administration, but is useful in examining larger public policy issues associated with an aging workforce. ^

A model of school managerial control : the systematic analysis of managerial behaviors, processes, and indicators

The theoretical construct of control has been defined as necessary (Etzioni, 1965), ubiquitous (Vickers, 1967), and on-going (E. Langer, 1983). Empirical measures, however, have not adequately given meaning to this potent construct, especially within complex organizations such as schools. Four stages of theory-development and empirical testing of school building managerial control using principals and teachers working within the nation's fourth largest district are presented in this dissertation as follows: (1) a review and synthesis of social science theories of control across the literatures of organizational theory, political science, sociology, psychology, and philosophy; (2) a systematic analysis of school managerial activities performed at the building level within the context of curricular and instructional tasks; (3) the development of a survey questionnaire to measure school building managerial control; and (4) initial tests of construct validity including inter-item reliability statistics, principal components analyses, and multivariate tests of significance. The social science synthesis provided support of four managerial control processes: standards, information, assessment, and incentives. The systematic analysis of school managerial activities led to further categorization between structural frequency of behaviors and discretionary qualities of behaviors across each of the control processes and the curricular and instructional tasks. Teacher survey responses (N=486) reported a significant difference between these two dimensions of control, structural frequency and discretionary qualities, for standards, information, and assessments, but not for incentives. The descriptive model of school managerial control suggests that (1) teachers perceive structural and discretionary managerial behaviors under information and incentives more clearly than activities representing standards or assessments, (2) standards are primarily structural while assessments are primarily qualitative, (3) teacher satisfaction is most closely related to the equitable distribution of incentives, (4) each of the structural managerial behaviors has a qualitative effect on teachers, and that (5) certain qualities of managerial behaviors are perceived by teachers as distinctly discretionary, apart from school structure. The variables of teacher tenure and school effectiveness reported significant effects on school managerial control processes, while instructional levels (elementary, junior, and senior) and individual school differences were not found to be significant for the construct of school managerial control.