Taxi Time Comparison Before and After Surface Metering Using ASPM Data

In this taxi time analysis, the ASPM data before and after surface metering were compared to see the effects of surface metering on taxi-out/in times. Results show that the surface metering from ATD-2 technologies did no harm in taxi-out times at CLT. This was originally presented to the ATD-2 Analytics team in April 2018

A Study of Tradeoffs in Scheduling Terminal-Area Operations

The terminal area surrounding an airport is an important component of the air transportation system, and efficient terminal-area schedules are essential for accommodating the projected increase in air traffic demand. Aircraft arrival schedules are subject to a variety of operational constraints, such as minimum separation for safety, required arrival time-windows, limited deviation from a first-come firstserved sequence, and precedence constraints. There is also a range of objectives associated with multiple stakeholders that could be optimized in these schedules; the associated tradeoffs are evaluated in this paper. A dynamic programming algorithm for determining the minimum cost arrival schedule, given aircraft-dependent delay costs, is presented. The proposed approach makes it possible to determine various tradeoffs in terminal-area operations. A comparison of maximum throughput and minimum average delay schedules shows that the benefit from maximizing throughput could be at the expense of an increase in average delay, and that minimizing delay is the more advantageous of the two objectives in most cases. A comprehensive analysis of the tradeoffs between throughput and fuel costs and throughput and operating costs is conducted, accounting for both the cost of delay (as reported by the airlines) and the cost of speeding up when possible (from models of aircraft performance)

Tradeoff evaluation of scheduling algorithms for terminal-area air traffic control

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 117-120).The terminal-area surrounding an airport is an important component of the air transportation system, and efficient and robust terminal-area schedules are essential for successfully meeting the projected increase in air traffic demand. Aircraft arrival schedules are subject to a variety of operational constraints, such as minimum separation requirements for safety, required arrival time-windows, limited deviation from a nominal or FCFS sequence, and precedence constraints on the arrival order. With these constraints, there is a range of desirable objectives associated with multiple stakeholders that could be optimized in these schedules. The schedules should also be robust against the uncertainty around the terminal-area. A dynamic programming algorithm for determining the minimum cost arrival schedule, given the aircraft-dependent delay costs, is presented in this thesis. The proposed approach makes it possible to determine various tradeoffs between multiple objectives in terminal-area operations. The comparison of schedules that maximize throughput to those that minimize average delay shows that the benefit from maximizing throughput could be at the expense of an increase in average delay, and that minimizing average delay is the more advantageous of the two objectives in most cases. A comprehensive analysis of the tradeoffs between throughput and fuel costs, and throughput and operating costs is conducted, accounting for both the cost of delay (as reported by the airlines) and the cost of speeding-up when possible (from models of aircraft performance). It is also demonstrated that the proposed aircraft scheduling algorithm can be applied to the optimization problem for the coupled operations of arrivals and departures on a single runway.(cont.) Using the same framework, a dynamic programming algorithm for robust scheduling in terminal-area is also developed. This algorithm is designed to minimize the possibility that an air traffic controller has to intervene the initially determined schedule under the uncertainty of the landing time accuracy due to the aircraft equipage. The result from the proposed approach is a tradeoff curve between runway throughput and robustness.by Hanbong Lee.S.M

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment

Initial Data Analysis Results for ATD-2 ISAS HITL Simulation

To evaluate the operational procedures and information requirements for the core functional capabilities of the ATD-2 project, such as tactical surface metering tool, APREQ-CFR procedure, and data element exchanges between ramp and tower, human-in-the-loop (HITL) simulations were performed in March, 2017. This presentation shows the initial data analysis results from the HITL simulations. With respect to the different runway configurations and metering values in tactical surface scheduler, various airport performance metrics were analyzed and compared. These metrics include gate holding time, taxi-out in time, runway throughput, queue size and wait time in queue, and TMI flight compliance. In addition to the metering value, other factors affecting the airport performance in the HITL simulation, including run duration, runway changes, and TMI constraints, are also discussed

Scheduler Design Criteria: Requirements and Considerations

This presentation covers fundamental requirements and considerations for developing schedulers in airport operations. We first introduce performance and functional requirements for airport surface schedulers. Among various optimization problems in airport operations, we focus on airport surface scheduling problem, including runway and taxiway operations. We then describe a basic methodology for airport surface scheduling such as node-link network model and scheduling algorithms previously developed. Next, we explain how to design a mathematical formulation in more details, which consists of objectives, decision variables, and constraints. Lastly, we review other considerations, including optimization tools, computational performance, and performance metrics for evaluation

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics