A fuzzy reasoning-based sequencing of arrival aircraft in the terminal area

Abstract

A fuzzy reasoning-based method for scheduling air traffic in the terminal area has been designed at the NASA Ames Research Center and operationally tested at the Dallas/Fort Worth Terminal Radar Approach Control (TRACON). The scheduling system sequences and assigns landing times to arrival aircraft by utilizing continuous updates of aircraft radar data and controller inputs. The scheduling algorithm contains a knowledge base which was refined during several thousand hours of controller-in-the-loop real-time simulations. This knowledge base applies fuzzy reasoning to evaluate propositions that consider both performance criteria and workload criteria, such as delay reduction and conflict avoidance. Operational test results show that the algorithm determines an efficient arrival plan to land aircraft in a manner acceptable to the air traffic controllers. This paper details the scheduling algorithm and presents data regarding its effectiveness in predicting the landing order of arrival aircraft.

Receding Horizon Control for Aircraft Arrival Sequencing and Scheduling

“…Remark 1: There are many existing algorithms that can be modified and used as online optimizer to solve the minimization problem (3) (for example, see [10], [19], and [22]). Those algorithms are different in terms of computational efficiency and optimality.…”

“…In [3], individual airline priorities among incoming flights are taken into account in ASS, and the concept of "priority scheduling" is then defined as the scheduling of a bank of arrival aircraft according to a preferred order of arrival. The most widely accepted concept in the practices of ASS is "position shifting" [4]- [11], [17]- [22], which is based on two facts: first, safety regulations state that any two coaltitudinal aircraft must maintain a "minimum horizontal separation," which is a function of the types and of the relative positions of the two aircraft; second, the "landing speed" of a type of aircraft is generally different from that of another type of aircraft. As a consequence of the variability of the above parameters, the landing time interval (LTI), which is the minimum permissible time interval between two successive landings, is a variable quantity.…”

“…There are a number of papers studying the problem of "position shifting" ASS (for example, see [6]- [11], [17]- [22]). Dear [6] presents an excellent investigation of the ASS.…”

“…Based on the result of [19], the dynamic case is investigated in [20] by introducing displacement function, which is used to explicitly link each new decision back to the previous decisions. Fuzzy theory is attempted in [22], where experts' knowledge of the ASS problem is used to form the core of fuzzy reasoning mechanism. In [9], three models and corresponding algorithms are developed for the Boston Logan Airport terminal airspace to expedite the landing of incoming aircraft.…”

Surface Operation Benefits of a Collaborative Automation Concept

“…The SOAR concept assumes that runway assignment and scheduled arrival and departure times are either provided separately by other systems such as the Final Approach Spacing Tool (FAST) [9]- [11] or the Expedite Departure Path (EDP) tool [12], or new functions in the GO-SAFE system. For the purpose of the evaluation, the runway assignment is done manually as part of the traffic data preprocessing.…”