Classification of Days Using Weather Impacted Traffic in the National Airspace System

Abstract

Classification of days based on weather impact on the National Airspace System is essential to evaluate the effectiveness of traffic management decisions in the past, which ultimately can improve the operational readiness when similar events occur in the future. To achieve this goal, this paper presents a methodology to classify days based on severe weather impact on traffic. A daily index of the impact of severe weather on scheduled traffic flow, termed as the Weather Impacted Traffic Index, is used as an input to perform the classification. First, a factor analysis is performed to identify the dominant weather patterns that occur on various days. Six major weather factors are identified based on this analysis. Factor scores are obtained for each day based on the day’s weather location and severity. Days are clustered using Ward’s minimum-variance method applied to the daily factor scores. The outcome of the analysis is a set of 21 clusters and days within each cluster. While the weather and traffic in the days belonging to a common cluster are similar, they are not completely identical. Following the classification of days, the reroute advisories are analyzed to identify the frequently used routes on days belonging to various clusters. It is observed that the most frequently used reroutes on days that belong to a particular cluster exhibit similarity to the National Playbook routes designed to mitigate weather impact on those days, which is an intuitive result that is supported by data analysis. I. INTRODUCTION dverse weather reduces the capacity of the National Airspace System (NAS) by partially or completely blocking routes, waypoints, and airports. During such conditions, traffic managers at the FAA’s Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines’ Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. The performance of the TFM control actions is measured using a set of metrics such as total delay, cancellations, diversions, additional flying time, airborne holding time, loss of predictability of operations, etc. These performance metrics vary from day-to-day based on the severity, location, and characteristics of weather as well as the effectiveness of TFM control actions. If a particular day can be characterized as being similar, in terms of weather and traffic, to a few days in the past, then the TFM control actions from those days could serve as a basis for strategizing TFM on the current day of operation. A thorough post-operational evaluation of TFM actions in the past can reveal the potential areas of improvement, if possible. Doing so will better equip the NAS users (i.e., airlines) and the service provider (i.e., ATCSCC) with information to mitigate weather impact, and hence, improve the operational readiness. It will also improve the predictability of TFM control actions if the weather forecasts are reasonably accurate on a given day. A successful classification of days is necessary to evaluate the effectiveness of TFM actions on days that are similar. This paper presents a methodology to classify days based on weather and traffic pattern and to cluster them into groups. Days belonging to the same cluster may not be identical, but are statistically close enough. The Weather Impacted Traffic Index (WITI) measures the location and severity of weather and its impact on traffic.