Ordinal Regression Model for Forecasting En Route Controller Requirements
A model for forecasting enroute controller requirements was developed by Tien and Schonfeld [1] for incorporation into the FAA's NAS Strategy Simulator. In this study, one critical component in their work is improved by using probit regression model in order to more reasonably predict the number of controller positions per sector. Probabilities of using between 0 and 4 controllers per sector are estimated from the controller work history based on traffic densities and other sector characteristics. The improved controller estimation model is then demonstrated for its practicability and can provide an estimate of annual controller requirements in response to future traffic growth and capacity for evaluating future enroute performance.
Abstract There is a greater need than ever for the ability to accurately model urban system impacts resulting around the planet. Rapid urbanization is transforming landscapes from vegetation to an engineered infrastructure and thus altering land cover and land use. These alterations impact urban and global climate change, energy demand, human health, and ecological service functions. This article presents an overview of a refined land-cover classification protocol that seeks to refine current land-cover classifications of engineered paved surfaces. This new approach provides those who model urban systems and engineer the environment as well as other scientists and policy makers an expanded understanding of how intervention to the system can most effectively be accomplished through enhanced modeling. An object-oriented analysis regime is presented for an industrial park utilizing commercial software in conjunction with multispectral and panchromatic Quickbird satellite imagery. A detailed examination of ho...