This paper presents a new concept of optimized surface operations at busy airports to improve the efficiency of taxi operations, as well as reduce environmental impacts. The suggested system architecture consists of the integration of two decoupled optimization algorithms. The Spot Release Planner provides sequence and timing advisories to tower controllers for releasing departure aircraft into the movement area to reduce taxi delay while achieving maximum throughput. The Runway Scheduler provides take-off sequence and arrival runway crossing sequence to the controllers to maximize the runway usage. The description of a prototype implementation of this integrated decision support tool for the airport control tower controllers is also provided. The prototype decision support tool was evaluated through a human-in-the-loop experiment, where both the Spot Release Planner and Runway Scheduler provided advisories to the Ground and Local Controllers. Initial results indicate the average number of stops made by each departure aircraft in the departure runway queue was reduced by more than half when the controllers were using the advisories, which resulted in reduced taxi times in the departure queue.
At busy airports, air traffic controllers seek to find schedules for aircraft at the runway that aim to minimize delays of the aircraft while maximizing runway throughput. In reality, finding optimal schedules by a human controller is hard to accomplish since the number of feasible schedules available for the scheduling problem is quite large. In this paper, we pose this problem as a multiobjective optimization problem, with respect to total aircraft delay and runway throughput. Using principles of multiobjective dynamic programming, we develop an algorithm to find a set of Pareto-optimal solutions that completely specify the nondominated frontier. In addition to finding these solutions, this paper provides a proof of the algorithm's correctness and gives an analysis of its performance against a baseline algorithm using the operational data for a model of the Dallas/Fort Worth International Airport.Index Terms-Aircraft scheduling, multiple objective dynamic programming, Pareto-optimality, runway scheduling.
The National Aeronautics and Space Administration (NASA) is developing automation for managing flight traffic on the airport surface to reduce taxi times and increase traffic throughput, without compromising safety. The scheduler is the part of the automation that calculates the advisories that assist the controller with clearing, holding, and sequencing flights. The Surface Operations Simulator and Scheduler (SOSS) is a fast-time airport surface operations simulator that connects to schedulers. SOSS is used to develop and test schedulers to determine if they can produce benefits. To show that schedulers developed with SOSS are credible, a validation of SOSS was performed to demonstrate that it is an accurate model of real operations. Surveillance and Federal Aviation Administration (FAA) operational performance data recorded from real operations at Charlotte Douglas International Airport were used to build a SOSS traffic scenario. The traffic scenario was run through SOSS to create simulated flight tracks. The flight tracks were analyzed to generate simulated taxi time and runway throughput metrics. Actual taxi time and runway throughput metrics were generated from the surveillance and FAA operational performance data. The simulated and actual metrics were compared. After the initial simulation, the average difference between simulated and actual taxi times on a flight by flight basis was not zero. A model tuning was performed by running the SOSS simulation multiple times while varying SOSS parameters to drive the average difference between the simulated and actual taxi times to zero. The SOSS parameters used were the pushback duration times and the taxi and ramp target speeds. Results show that the average difference between the simulated and actual taxi times was driven to zero. In addition, the standard deviations of the simulated taxi times and the actual taxi times were almost the same. However, the standard deviation of the flight by flight taxi time differences was large. This is because SOSS cannot simulate on an individual flight basis the exact actions taken by each flight in reality, which is an issue for all simulators. Despite this issue, SOSS was found to be a statistically accurate simulation of real airport operations, and schedulers developed and tested using SOSS have potential for producing benefits in real airport traffic management automation systems. NomenclatureASQP = Airline Service Quality Performance ASDE-X = Airport Surface Detection Equipment, Model X 1 Aerospace Engineer, Aerospace High Density Operations Branch, Mail Stop 210-15, Senior member. 2 ATCT = Air Traffic Control Tower CAI = Common Algorithm Interface CLT = airport code for Charlotte Douglas International Airport FAA = Federal Aviation Administration NASA = National Aeronautics and Space Administration SOSS = Surface Operations Simulator and Scheduler I. IntroductionASA is researching and developing automation that provides advisories to ramp, ground, and local controllers, who manage air traffic on the airport surface. 1 ...
Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.
A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway flight sequences that are optimal for both runway throughput and delay. Realistic timebased operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour and can be 1000x faster.
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