The purpose of this research is to solve several important problems of the AVI system, including the AVI site location problem, travel time forecasting, the study of reliability and accuracy of the forecasted travel time. This thesis serves as a further research toward the modeling of AVI systems in which the effects of AVI site location, AVI site density, travel time forecasting are analyzed. The model based on the genetic algorithms was applied to AVI site location problem to solve it as a multi-objective optimization problem, thus the best locations was determined on the basis of several criteria. The model developed was tested in an assumed transportation network. The achieved CPU time in this stage of the research are promising. MATLAB and its accompanying Neural Network Toolbox, has been applied to data obtained from San Antonio real time AVI Tag database to forecast travel time. The approach to the neural network is detailed in this paper. Two ANN models were tested in this research. The accuracy of AVI travel time forecasting was then assessed and the better model for travel time forecasting was found. Lastly, a comparison of forecasted travel time with different travel time prediction technologies was performed to serve as a reference parameter for the travel time forecasting study. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / While optimization techniques have been studied since 300 B.C. when Euclid first considered the minimal distance between a point and a line, it wasn’t until 1966 that location optimization was first applied to a problem in healthcare. Location optimization techniques are capable of increasing efficiency and equity in the placement of many types of services, including those within the healthcare industry, thus enhancing quality of life. Medical imaging is a healthcare service which helps to determine medical diagnoses in acute and preventive care settings. It provides physicians with information guiding treatment and returning a patient back to optimal health. In this study, a retrospective analysis of the locations of current medical imaging centers in central Indiana is performed, and alternate placement as determined using optimization techniques is considered and compared. This study focuses on reducing the drive time experienced by the population within the study area to their nearest imaging facility. Location optimization models such as the P-Median model, the Maximum Covering model, and Clustering and Partitioning are often used in the field of operations research to solve location problems, but are lesser known within the discipline of Geographic Information Science. This study was intended to demonstrate the capabilities of these powerful algorithms and to increase understanding of how they may be applied to problems within healthcare. While the P-Median model is effective at reducing the overall drive time for a given network set, individuals within the network may experience lengthy drive times. The results further indicate that while the Maximum Covering model is more equitable than the P-Median model, it produces large sets of assigned individuals overwhelming the capacity of one imaging center. Finally, the Clustering and Partitioning method is effective at limiting the number of individuals assigned to a given imaging center, but it does not provide information regarding average drive time for those individuals. In the end, it is determined that a capacitated Maximal Covering model would be the preferred method for solving this particular location problem.