Weather impact on airport arrival meter fix throughput

Abstract

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers impose regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently put in holding patterns until they can be rerouted. A model that predicts the weather-impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at Newark Liberty International Airport (EWR). Several scenarios of weather-impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughput are examined. These weather translation models are developed and validated using EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather-impacted throughput at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughput than that produced by multiple linear regression method.