Congestion at major airports worldwide results in increased taxi times, fuel burn, and emissions. Regulating the pushback of aircraft from their gates, also known as departure metering, is a promising approach to mitigating surface congestion. Departure metering algorithms require models of airport surface traffic and knowledge of when a flight would be to be ready for pushback, which is called the earliest off-block time (EOBT). While EOBTs are known to be inaccurate due to several reasons, there has been little prior research on characterizing EOBT uncertainty and its impact on departure metering. We present a new class of queuing network models for the airport surface that are capable of capturing congestion at multiple locations. We demonstrate our modeling approach using operational data from three major U.S. airports: Newark Liberty International Airport, Dallas/Fort Worth International Airport, and Charlotte Douglas International Airport. We analyze the current levels of uncertainty in the EOBT information published by the airlines and conduct a parametric analysis of the reduction in departure metering benefits due to errors in the EOBT information. Our analysis indicates that the current levels of EOBT uncertainty lead to a 50% reduction in benefits at some airports when compared with an ideal case with no EOBT uncertainty. Two approaches to departure metering are considered: the National Aeronautics and Space Administration’s Airspace Technology Demonstration-2 logic and a new optimal control approach. We show that our queuing network models can help design and evaluate both approaches and that the optimal control approach is more effective in accommodating EOBT uncertainty while maintaining runway utilization.

The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap-fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–2017 are processed using both the specific attenuation [R(A)] and reflectivity-differential reflectivity [R(Z,ZDR)] QPE methods and are compared against Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified based on beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates based on 2-year/1-hr return rates are used for a CONUS-wide analysis of QPE errors in extreme rainfall situations. These errors are then re-quantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Finally, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance.

The assessment of the fuel burn and emissions impact of airport surface operations is a key part of understanding the environmental impacts of aviation. These assessments are needed at two levels: the analysis of inventories (the total amount of fuel burned and emissions discharged over some period of time), and the analysis of spatial distributions (the amount of emissions experienced at a particular location within or near the airport). Although the availability of taxi times for the operations of interest is sufficient for inventory analysis, the analysis of spatial distributions requires estimates of where on the airport surface an aircraft is located as it consumes fuel. In this paper, we show how a data-driven queuing network model can be developed in order to estimate the time that an aircraft spends at different congested locations on the airport surface. These models are useful both in spatial distribution analysis and in accurately predicting taxi times in the absence of measurements (for example, for projected demand sets). We use measurements of ultrafine particles at Los Angeles International Airport to demonstrate that the proposed model can help predict the measured emissions at different monitoring sites located in the vicinity of the airport. In the process, we show how one could develop a machine learning model of the spatial distribution of airport surface emissions given the pollutant measurements, air traffic demand, and prevailing weather conditions. Finally, we develop a clustering-based method to evaluate the generalizability of our surface operations modeling framework.

No abstract