Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution. In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior. Keywords: Air transportation; Congestion control; Large-scale optimization; Data-driven modeling; Human decision processe

A Distributed Framework for Traffic Flow Management in the Presence of Unmanned Aircraft

The integration of unmanned aircraft systems (UAS) into the airspace system is a key challenge facing air traffic management today. An important aspect of this challenge is how to determine and manage 4-dimensional trajectories for both manned and unmanned aircraft, and how to appropriately allocate resources among different aircraft. An integrated approach requires solving the traditional Air Traffic Flow Management (ATFM) problem to balance the capacity and demand of airport and airspace resources, but at a significantly larger scale. In doing so, aircraft connectivity constraints of commercial flights must be satisfied. In addition to these and the resource capacity constraints, geofencing constraints for unmanned aircraft that keep them within or outside a certain region of the airspace, must also be incorporated. This paper presents a distributed implementation of an integer programming approach for solving large-scale ATFM problems in the presence of unmanned aircraft. Given desired mission plans and flight-specific operating and delay costs, the proposed approach uses column generation to determine optimal trajectories in space and time, in the presence of network and flight connectivity constraints, airport and airspace capacity constraints, and geofencing constraints. Using projected demand for the year 2030 from the United States with approximately 48, 000 passenger flights and 29, 000 UAS operations (on a wide range of missions) per day, we show that our implementation can find nearly-optimal trajectories for a 24-hour period in less than 4 minutes. Furthermore, a rolling horizon implementation (with 6-8 hour time windows) results in run times of less than a minute. In addition to being the largest instances of the ATFM problem solved to date, these results represent the first effort to incorporate UAS trajectories into airspace and airport resource sharing problems.United States. National Aeronautics and Space Administration (Small Business Innovative Grant

On the Probabilistic Modeling of Runway Inter-departure Times

This paperr examines the validity of the Erlang distribution for runway service times. It uses high-fidelity surface surveillance data, for the first time, to model the probability distributions of runway service times and departure throughput, and to validate the Erlang service time assumption. The paper proposes several potential approaches to determine departure runway service time distributions from empirical data, and compares the results. In particular, it finds that a displaced exponential fit may be a better match to the empirical service time distribution than an Erlang distribution. However, the computational benefits offered by the Erlang service time distribution, its accurate reflection of the means and variances of the empirical service time and throughput distributions, and its ability to represent the tail of the service time distribution, make it attractive for use in queuing models of airport operations.National Science Foundation (U.S.) (Cyber-Physical Systems Award 0931843

Identification of Robust Routes using Convective Weather Forcasts

Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System (NAS), especially during summer months when travel demand is high. This has been the motivation for Air Traffic Flow Management (ATFM) algorithms that optimize flight routes in the presence of reduced airspace and airport capacities. These models assume either the availability of reliable probabilistic weather forecasts or accurate predictions of robust routes; unfortunately, such forecasts do not currently exist. This paper adopts a data-driven approach that identifies robust routes and derives stochastic capacity forecasts from deterministic convective weather forecasts. Using techniques from machine learning and extensive data sets of forecast and observed convective weather, the proposed approach classifies routes that are likely to be viable in reality. The resultant model for route robustness can also be mapped into probabilistic airspace capacity forecasts.National Science Foundation (ECCS- 0745237)National Aeronautics and Space Administration NGATSATM Airspace Program (NNA06CN24A

Integrated Control of Airport and Terminal Airspace Operations

Airports are the most resource-constrained components of the air transportation system. This paper addresses the problems of increased flight delays and aircraft fuel consumption through the integrated control of airport arrival and departure operations. Departure operations are modeled using a network abstraction of the airport surface. Published arrival routes to airports are synthesized to form a realistic model of arrival airspace. The proposed control framework calculates the optimal times of departure of aircraft from the gates, as a function of the arrival and departure traffic as well as airport characteristics such as taxiway layout and gate capacity. The integrated control formulation is solved using dynamic programming, which allows calculation of policies for real-time implementation. The advantages of the proposed methodology are illustrated using simulations of Boston's Logan International Airport.National Science Foundation (U.S.) (0931843

Data-Driven Modeling of the Airport Configuration Selection Process

The runway configuration is the set of the runways at an airport that are used for arrivals and departures at any time. While many factors, including weather, expected demand, environmental considerations, and coordination of flows with neighboring airports, influence the choice of runway configuration, the actual selection decision is made by air traffic controllers in the airport tower. As a result, the capacity of an airport at any time is dependent on the behavior of human decision makers. This paper develops a statistical model to characterize the configuration selection decision process using empirical observations. The proposed approach, based on the discrete-choice modeling framework, identifies the influence of various factors in terms of the utility function of the decision maker. The parameters of the utility functions are estimated through likelihood maximization. Correlations between different alternatives are captured using a multinomial “nested logit” model. A key novelty of this study is the quantitative assessment of the effect of inertia, or the resistance to configuration changes, on the configuration selection process. The developed models are used to predict the runway configuration 3 h ahead of time, given operating conditions such as wind, visibility, and demand. Case studies based on data from Newark (EWR) and LaGuardia (LGA) airports show that the proposed model predicts runway configuration choices significantly better than a baseline model that only considers the historical frequencies of occurrence of different configurations.National Science Foundation (U.S.) (Grant 1239054

Evaluation of strategies for reducing taxi-out emissions at airports

Aircraft taxiing on the surface contribute significantly to the fuel burn and emissions at airports. This paper is aimed at estimating the baseline fuel burn and emissions from taxi-out processes at airports, evaluating the potential benefits of strategies proposed to reduce them, and assessing the critical implementation barriers that need to be overcome prior to the adoption of these approaches at airports. This study evaluates the effects of two different strategies, namely, single engine taxiing and operational tow-outs, as well as the total potential decrease in taxi times, fuel burn and emissions that may be obtained through various operational improvements at airports. The baseline emission calculation is done for all domestic commercial flights in the US for the year 2007, and a comparative study of the effects of these strategies is performed for the top twenty airports (by number of departures) in the US

Design and simulation of airport congestion control algorithms

This paper proposes a stochastic model of runway departures and a dynamic programming algorithm for their control at congested airports. Using a multi-variable state description that includes the capacity forecast, the runway system is modeled as a semi-Markov process. The paper then introduces a queuing system for modeling the controlled departure process that enables the efficient calculation of optimal pushback policies using decomposition techniques. The developed algorithm is simulated at Philadelphia International Airport, and compared to other potential control strategies including a threshold-policy. The algorithm is also shown to effectively adapt to changes in airport departure capacity, maintain runway utilization and efficiently manage congestion.National Science Foundation (U.S.) (Award 0931843

Estimation of maximum-likelihood discrete-choice models of the runway configuration selection process

The runway configuration is the subset of the runways at an airport that are used for arrivals and departures at any time. Many factors, including weather (wind and visibility), expected arrival and departure demand, environmental considerations such as noise abatement procedures, and coordination of flows with neighboring airports, govern the choice of runway configuration. This paper develops a statistical model to characterize this process using empirical observations. In particular, we demonstrate how a maximum-likelihood discrete-choice model of the runway configuration process can be estimated using aggregate traffic count and other archived data at an airport, that are available over 15 minute intervals. We show that the estimated discrete-choice model not only identifies the influence of various factors in decision-making, but also provides significantly better predictions of runway configuration changes than a baseline model based on the frequency of occurrence of different configurations. The approach is illustrated using data from Newark (EWR) and LaGuardia (LGA) airports

Scheduling Aircraft Landings under Constrained Position Shifting

Optimal scheduling of airport runway operations can play an important role in improving the safety and efficiency of the National Airspace System (NAS). Methods that compute the optimal landing sequence and landing times of aircraft must accommodate practical issues that affect the implementation of the schedule. One such practical consideration, known as Constrained Position Shifting (CPS), is the restriction that each aircraft must land within a pre-specified number of positions of its place in the First-Come-First-Served (FCFS) sequence. We consider the problem of scheduling landings of aircraft in a CPS environment in order to maximize runway throughput (minimize the completion time of the landing sequence), subject to operational constraints such as FAA-specified minimum inter-arrival spacing restrictions, precedence relationships among aircraft that arise either from airline preferences or air traffic control procedures that prevent overtaking, and time windows (representing possible control actions) during which each aircraft landing can occur. We present a Dynamic Programming-based approach that scales linearly in the number of aircraft, and describe our computational experience with a prototype implementation on realistic data for Denver International Airport