Improving Analytical Travel Time Estimation for Transportation Planning Models

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

The relationship of aptitude, course grades, and program length, to performance on a standards based test (NCLEX -RN)

The purpose of this study was threefold: first, to investigate variables associated with learning, and performance as measured by the National Council Licensure Examination for Registered Nurses (NCLEX-RN). The second purpose was to validate the predictive value of the Assessment Technologies Institute (ATI) achievement exit exam, and lastly, to provide a model that could be used to predict performance on the NCLEX-RN, with implications for admission and curriculum development. The study was based on school learning theory, which implies that acquisition in school learning is a function of aptitude (pre-admission measures), opportunity to learn, and quality of instruction (program measures). Data utilized were from 298 graduates of an associate degree nursing program in the Southeastern United States. Of the 298 graduates, 142 were Hispanic, 87 were Black, non-Hispanic, 54 White, non-Hispanic, and 15 reported as Others. The graduates took the NCLEX-RN for the first time during the years 2003–2005. This study was a predictive, correlational design that relied upon retrospective data. Point biserial correlations, and chi-square analyses were used to investigate relationships between 19 selected predictor variables and the dichotomous criterion variable, NCLEX-RN. The correlation and chi square findings indicated that men did better on the NCLEX-RN than women; Blacks had the highest failure rates, followed by Hispanics; older students were more likely to pass the exam than younger students; and students who passed the exam started and completed the nursing program with a higher grade point average, than those who failed the exam. Using logistic regression, five statistical models that used variables associated with learning and student performance on the NCLEX-RN were tested with a model adapted from Bloom's (1976) and Carroll's (1963) school learning theories. The derived model included: NCLEX-RNsuccess = f (Nurse Entrance Test and advanced medical-surgical nursing course grade achieved). The model demonstrates that student performance on the NCLEX-RN can be predicted by one pre-admission measure, and a program measure. The Assessment Technologies Institute achievement exit exam (an outcome measure) had no predictive value for student performance on the NCLEX-RN. The model developed accurately predicted 94% of the student's successful performance on the NCLEX-RN.