AVIATION CONGESTION MANAGEMENT IMPROVEMENTS IN MODELING THE PREDICTION, MITIGATION, AND EVALUATION OF CONGESTION IN THE NATIONAL AIRSPACE SYSTEM

The air transportation system in the United States is one of the most complex systems in the world. Projections of increasing air traffic demand in conjunction with limited capacity, that is volatile and affected by exogenous random events, represent a major problem in aviation system management. From a management perspective, it is essential to make efficient use of the available resources and to create mechanisms that will help alleviate the problems of the imbalance between demand and capacity. Air traffic delays are always present and the more air traffic increases the more the delays will increase with very unwanted economic impacts. It is of great interest to study them further in order to be able to more effectively mitigate them. A first step would be to try to predict them under various circumstances. A second step would be to develop various mechanisms that will help in reducing delays in different settings. The scope of this dissertation is to look closer at a threefold approach to the problem of congestion in aviation. The first effort is the prediction of delays and the development of a model that will make these predictions under a wide variety of distributional assumptions. The work presented here is specifically on a continuum approximation using diffusion methods that enables efficient solutions under a wide variety of distributional assumptions. The second part of the work effort presents the design of a parsimonious language of exchange, with accompanying allocation mechanisms that allow carriers and the FAA to work together quickly, in a Collaborative Decision Making environment, to allocate scarce capacity resources and mitigate delays. Finally, because airlines proactively use longer scheduled block times to deal with unexpected delays, the third portion of this dissertation presents the assessment of the monetary benefits due to improvements in predictability as manifested through carriers' scheduled block times

Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations

Planning and Deploying Transit Signal Priority in Small and Medium-Sized Cities: Burlington, Vermont, Case Study

Innovations in traffic signal systems have generated a great deal of interest in the provision of preferential traffic signal strategies and treatments for transit buses and other vehicles at signalized intersections in cities of all sizes. The primary objective of this paper is three fold: 1) to synthesize the literature of the lessons learned associated with planning and deploying transit signal priority (TSP) strategies in small and medium-sized cities; 2) to demonstrate the application of a micro-simulation model, VISSIM, to assess transit priority impacts in small and medium-sized communities where the required VISSIM input data are often limited; and 3) to present guidelines to aid traffic engineers and transit planners who are considering TSP strategies in small and medium-sized cities. An underlying aim of this paper is to recognize the differences in transit priority planning and deployment in small and medium-sized cities as compared to major metropolitan areas

Planning and Deploying Transit Signal Priority in Small and Medium-Sized Cities: Burlington, Vermont, Case Study

Innovations in traffic signal systems have generated a great deal of interest in the provision of preferential traffic signal strategies and treatments for transit buses and other vehicles at signalized intersections in cities of all sizes. The primary objective of this paper is three fold: 1) to synthesize the literature of the lessons learned associated with planning and deploying transit signal priority (TSP) strategies in small and medium-sized cities; 2) to demonstrate the application of a micro-simulation model, VISSIM, to assess transit priority impacts in small and medium-sized communities where the required VISSIM input data are often limited; and 3) to present guidelines to aid traffic engineers and transit planners who are considering TSP strategies in small and medium-sized cities. An underlying aim of this paper is to recognize the differences in transit priority planning and deployment in small and medium-sized cities as compared to major metropolitan areas

Use of Queuing Models to Estimate Delays Savings from 4D Trajectory Precision

Abstract-The potential benefit from introducing trajectory based operations into the NAS is estimated in this paper. Delay predictions of a stochastic and a deterministic queuing model, which represent high and low levels of trajectory uncertainty, are compared. It is found that delay savings are on the order of 35% in the average case, Delay predictions from the various models are found to be strongly collinear over a wide range of congestion levels

Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations