Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

Simulation evaluation of TIMER, a time-based, terminal air traffic, flow-management concept

A description of a time-based, extended terminal area ATC concept called Traffic Intelligence for the Management of Efficient Runway scheduling (TIMER) and the results of a fast-time evaluation are presented. The TIMER concept is intended to bridge the gap between today's ATC system and a future automated time-based ATC system. The TIMER concept integrates en route metering, fuel-efficient cruise and profile descents, terminal time-based sequencing and spacing together with computer-generated controller aids, to improve delivery precision for fuller use of runway capacity. Simulation results identify and show the effects and interactions of such key variables as horizon of control location, delivery time error at both the metering fix and runway threshold, aircraft separation requirements, delay discounting, wind, aircraft heading and speed errors, and knowledge of final approach speed

Potential impacts of advanced technologies on the ATC capacity of high-density terminal areas

Advanced technologies for airborne systems (automatic flight control, flight displays, navigation) and for ground ATC systems (digital communications, improved surveillance and tracking, automated decision-making) create the possibility of advanced ATC operations and procedures which can bring increased capacity for runway systems. A systematic analysis is carried out to identify certain such advanced ATC operations, and then to evaluate the potential benefits occurring over time at typical US high-density airports (Denver and Boston). The study is divided into three parts: (1) A Critical Examination of Factors Which Determine Operational Capacity of Runway Systems at Major Airports, is an intensive review of current US separation criteria and terminal area ATC operations. It identifies 11 new methods to increase the capacity of landings and takeoffs for runway systems; (2) Development of Risk Based Separation Criteria is the development of a rational structure for establishing reduced ATC separation criteria which meet a consistent Target Level of Safety using advanced technology and operational procedures; and (3) Estimation of Capacity Benefits from Advanced Terminal Area Operations - Denver and Boston, provides an estimate of the overall annual improvement in runway capacity which might be expected at Denver and Boston from using some of the advanced ATC procedures developed in Part 1. Whereas Boston achieved a substantial 37% increase, Denver only achieved a 4.7% increase in its overall annual capacity

Information transfer problems in the aviation system

Problems in the transfer of information within the aviation system are discussed. Particular attention is given to voice communication problems in both intracockpit and air/ground situations

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

Observation and Analysis of Departure Operations at Boston Logan International Airport

The Departure Planner (DP) is a concept for a decision-aiding tool that is aimed at improving the departure operations performance at major congested airports. In order to support the development of the DP tool, the flow constraints and their causalities in the departure process - primarily responsible for generating inefficiencies and delays- need to be identified. This thesis is an effort to identify such flow constraints and gain a deep understanding of the underlying dynamics of the departure process based on field observations and data analysis at Boston Logan International Airport. It was observed that the departure process is a complex interactive queuing system, where aircraft queues form as a manifestation of the flow constraints. While departure delays were observed in all airport components (runways, taxiways, ramps and gates), it was concluded that the flow constraints manifest mainly at the runway system, which exhibits the largest delays and queues. Major delays and inefficiencies were also observed due to downstream flow constraints, which propagate back and block the departure flow from the airport. It was also observed that the airport system is a highly controlled system as the air traffic controllers manage the flow constraints. The air traffic controllers were, therefore, identified as another flow constraint due to their workload and their main strategies in managing the flow constraints were observed. Based on the observations, a core departure process was identified consisting of two main elements: a queuing element generated by the flow constraints and a control element representing the air traffic controller actions. This core process was abstracted using a controlled queuing framework, where the air traffic controller actions are represented by blocking the flow of aircraft in order to maintain safe operation of the airport resources according to the ATC rules and procedures and regulate the outbound flow to constrained downstream resources. The controlled queuing framework was used to analyze the departure process highlighting the queuing dynamics and the control behavior for different flow constraint examples. In conclusion, a number of implications for the Departure Planner and other improved methods for departure operations are inferred from the observations and analysis.This work was supported by the National Aeronautics and Space Administration Ames Research Center under grant NAG 2-1128

Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties

This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. Analyzing flight arrival delay scenarios reveals strong multimodal distributions and clusters in arrival flight time durations. A multi-stage conditional ML predictor enhances separation time prediction based on flight events. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation, solved using mixed-integer linear programming (MILP). Historical flight recordings and model predictions address uncertainties between successive flights, ensuring reliability. The proposed method is validated using real-world data from the Atlanta Air Route Traffic Control Center (ARTCC ZTL). Case studies demonstrate an average 17.2% reduction in total landing time compared to the First-Come-First-Served (FCFS) rule. Unlike FCFS, the proposed methodology considers uncertainties, instilling confidence in scheduling. The study concludes with remarks and outlines future research directions

Real-time adaptive aircraft scheduling

One of the most important functions of any air traffic management system is the assignment of ground-holding times to flights, i.e., the determination of whether and by how much the take-off of a particular aircraft headed for a congested part of the air traffic control (ATC) system should be postponed in order to reduce the likelihood and extent of airborne delays. An analysis is presented for the fundamental case in which flights from many destinations must be scheduled for arrival at a single congested airport; the formulation is also useful in scheduling the landing of airborne flights within the extended terminal area. A set of approaches is described for addressing a deterministic and a probabilistic version of this problem. For the deterministic case, where airport capacities are known and fixed, several models were developed with associated low-order polynomial-time algorithms. For general delay cost functions, these algorithms find an optimal solution. Under a particular natural assumption regarding the delay cost function, an extremely fast (O(n ln n)) algorithm was developed. For the probabilistic case, using an estimated probability distribution of airport capacities, a model was developed with an associated low-order polynomial-time heuristic algorithm with useful properties

Trajectory Clustering and an Application to Airspace Monitoring

This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Nominal trajectories are determined and learned using data driven methods. Standard procedures are used by air traffic controllers (ATC) to guide aircraft, ensure the safety of the airspace, and to maximize the runway occupancy. Even though standard procedures are used by ATC, the control of the aircraft remains with the pilots, leading to a large variability in the flight patterns observed. Two methods to identify typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as standard. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is "healthy" when all aircraft are flying according to the nominal procedures. A measure of complexity is introduced, measuring the conformance of current flight to nominal flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure a safe separation between aircraft.Comment: 15 pages, 20 figure

Flight evaluation of LORAN-C in the State of Vermont

A flight evaluation of LORAN C as a supplement to existing navigation aids for general aviation aircraft, particularly in mountainous regions of the United States and where VOR coverage is limited was conducted. Flights, initiated in the summer months, extend through four seasons and practically all weather conditions typical of northeastern U.S. operations. Assessment of all the data available indicates that LORAN C signals are suitable as a means of navigation during enroute, terminal and nonprecision approach operations and the performance exceeds the minimum accuracy criteria