An investigation of location effect on trip production using artificial neural networks

As traffic congestion exuberates and new roadway construction is severely constrained because of limited availability of land, high cost of land acquisition, and communities' opposition to the building of major roads, new solutions have to be sought to either make roadway use more efficient or reduce travel demand. There is a general agreement that travel demand is affected by land use patterns. However, traditional aggregate four-step models, which are the prevailing modeling approach presently, assume that traffic condition will not affect people's decision on whether to make a trip or not when trip generation is estimated. Existing survey data indicate, however, that differences exist in trip rates for different geographic areas. The reasons for such differences have not been carefully studied, and the success of quantifying the influence of land use on travel demand beyond employment, households, and their characteristics has been limited to be useful to the traditional four-step models. There may be a number of reasons, such as that the representation of influence of land use on travel demand is aggregated and is not explicit and that land use variables such as density and mix and accessibility as measured by travel time and congestion have not been adequately considered. This research employs the artificial neural network technique to investigate the potential effects of land use and accessibility on trip productions. Sixty two variables that may potentially influence trip production are studied. These variables include demographic, socioeconomic, land use and accessibility variables. Different architectures of ANN models are tested. Sensitivity analysis of the models shows that land use does have an effect on trip production, so does traffic condition. The ANN models are compared with linear regression models and cross-classification models using the same data. The results show that ANN models are better than the linear regression models and cross-classification models in terms of RMSE. Future work may focus on finding a representation of traffic condition with existing network data and population data which might be available when the variables are needed to in prediction.

Doctoral Students: Online, On Time, and On to Graduation

Increased numbers of students are enrolling in online doctoral programs. Although students enroll for a variety of reasons, many do not persist to the dissertation phase. The results of this quantitative study can guide the development of retention strategies for students who are at risk of academic failure in doctoral programs.

Assessing the effects of professionals' career decision -making on their participation reasons in continuing education in Taiwan

The purpose of this study was to assess the relationship between working professionals' Career Decision-Making Self-Efficacy beliefs (CDMSE beliefs) and their reasons for participating in in-service master's level programs in Taiwan. ^ The data collection instruments used were Grotelueschen's (1985) Participation Reasons Scale (PRS), and Betz, Klein, and Taylor's (1996) Career Decision-Making Self-Efficacy-Short Form (CDMSE-SF), and a Demographic Data Form (DDF) developed specifically for this study. ^ Surveys were administered to 800 working professionals who participated in inservice master's level programs at 22 Taiwanese universities. The survey was conducted in May 2004. Data were analyzed by simple descriptive statistics, principal component factor analysis, and multiple regression. Four factors of participation reasons were found and five components of CDMSE beliefs were scored. ^ Five components of CDMSE beliefs are structured into the CDMSE-SF instrument: Self-Appraisal, Occupational Information, Goal-Selection, Planning, and Problem Solving. The reasons for participation found in this study were: Professional Improvement and Development, Professional Service, Personal Benefit and Job Security, and Professional Competence and Collegial Interaction. Pearson-product moment correlations revealed significant positive correlations between the five CDMSE subscales and the four factors of participation reasons. Multiple regression analysis revealed that participants' beliefs in their abilities to obtain information about occupations accounted for the preponderance of variance of scores on the Participation Reasons Scale (PRS). ^ This study concluded that professionals who believed that they were efficacious in obtaining information about occupations or professions tended to believe that the four reasons for participation represented by the factors of the PRS were important to them in making the decision to participate in continuing education. Additionally, it was noted that the reasons for participations for professionals who did not feel confident in their abilities to find such information could not be determined. ^ Recommendations are offered to assist those individuals responsible for developing recruiting programs in continuing education for professionals in Taiwan. These recommendations focus only on strategies intended to attract this target population of professionals who believe that they are efficacious in obtaining information about occupations. ^