Modeling time and space metering of flights in the National Airspace System

Metering flights at key points such as sector crossings is an important operational procedure in mitigating National Airspace System (NAS) traffic congestion due to high demand or changing weather conditions. The authors combine a mathematical model for minutes-in-trail or miles-in-trail (MIT) metering with discrete event simulation in a newly developed tool that can be used by analysts to examine or predict existing or developing bottlenecks within NAS. We define a penalty function recursively in terms of MIT delays between leading and following flights. With discrete event simulations, it is possible to examine the anticipated MIT delays for all the flights scheduled to arrive at any crossing point. Impacts of flight cancellations, route changes, and additional enroute delays as results of airport or sector congestion can all be evaluated during each simulation by updating the scheduled flight crossing times and the expected MIT delay penalties for all the trailing flights.

1 INTRODUCTION

The National Airspace System (NAS) handles over 50,000 daily flights. Scheduled flights may be connected through itineraries that show the flight legs traversed by a single airframe during the course of a day. If the originally intended itinerary is disrupted, e.g., by bad weather or excessive congestion, flights may be terminated, delayed, diverted, replaced, or rerouted to their departure airports. Flights may also be delayed, diverted, or metered while en route at key control points such as sector crossings, fixes, or waypoints. In addition, flights are also subject to handoff, sequencing, and metering, for events such as takeoff, landing, and sector crossing. Air traffic flow management (TFM) (Ball, Connolly, and Wanke 2003) procedures such as Ground Delay program (GDP), Ground Stop (GS), or Miles-in-Trail (MIT) metering are options available to the Air Traffic Management (ATM) authority to manage airway congestion and to respond to anticipated weather conditions (Wanke et al. 2003). The impacts of specific TFM actions on overall NAS performance can be measured with metrics such as flight delays and fuel use. Multiple simultaneous TFM actions may be highly interdependent, and the effects of a TFM action can ripple to other NAS resources and other flights during the day (Ostwald et al. 2003). The effects of such complex interactions can potentially be quantified with either discrete event simulation or mathematical models or both. In this analysis, the authors developed a recursive MIT penalty function to quantify the ripple effects of specific MIT programs over relevant sets of flights and flight restrictions within the NAS. In conjunction with discrete event simulation, it is possible to examine and quantify the total impacts of various TFM programs for alternatives analysis and provide a comparison across several alternative TFM programs available to air traffic flow management decision-makers. Combining the MIT penalty function with fast event-driven simulation, it is demonstrated that potential congestion “hot spots” in the NAS can be identified based on flight schedules. Potential or developing bottlenecks also can be simulated for anticipated or real weather conditions and the impacts of alternative GDP, GS, or MIT programs quantified either individually or collectively.