Traffic Flow Management Decision Research Articles

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.

Determining Stochastic Airspace Capacity for Air Traffic Flow Management

Deterministic air traffic flow management (TFM) decisions—the state of the art in terms of implementation—often result in unused airspace capacity. This is because the inherent uncertainties in weather predictions make it difficult to determine the number of aircraft that can be safely accommodated in a region of airspace during a given period. On the other hand, stochastic TFM algorithms are not amenable to implementation in practice due to the lack of valid stochastic mappings between weather forecasts and airspace capacity to serve as inputs to these algorithms. To fill this gap, we develop a fast simulation-based methodology to determine the stochastic capacity of a region of airspace using integrated weather-traffic models. The developed methodology consists of combining ensemble weather forecast information with an air traffic control algorithm to generate capacity maps over time. We demonstrate the overall methodology through a novel conflict resolution procedure and a simple weather scenario generation tool, and also discuss the potential use of ensemble weather forecasts. An operational study based on comparisons of the generated capacity distributions with observed impacts of weather events on air traffic is also presented.

Evaluation of an Integrated Traffic Flow Management Decision Making Approach

An integrated traffic flow management decision-making approach that sequentially schedules and reroutes flights subject to actual and forecast weather induced airspace capacity constraints is presented, and used to conduct 80 fast-time simulation experiments. These experiments considered variations in the severity and location of en route convective weather, and the traffic flow management strategies implemented to mitigate these weather impacts. Strategic flight scheduling, in which pre-departure and airborne delays were assigned to individual flights, was found to be effective at alleviating capacity constraints in sectors and at airports. However, tactical rerouting was found to be more effective than flight scheduling at avoiding en route weather hazards when the scheduling algorithms used weather impacted sector capacities as a proxy for weather location. In general, the distribution of the delays amongst the users was found to be most equitable when scheduling flights using a heuristic scheduling algorithm, such as ration-by-schedule. On the other hand, equity decreased by 77% when using a scheduling algorithm that took into account the number of seats aboard each flight. Interestingly, traffic flow management solutions that included scheduling and rerouting increased the equity of the solutions by 9% to 18% over comparable solutions that involved flight scheduling alone. Finally, the modeled results were compared with actual levels of delay, sector congestion and the number of weather incursions under three historical convective weather scenarios. The agreement between the modeled and actual results was found to be strongly dependent on the weather scenario and the number of constraints that were binding in the scheduling algorithm.

Modeling and Optimization in Traffic Flow Management

Traffic flow management (TFM) allocates the various airport, airspace, and other resources to maintain an efficient traffic flow consistent with safety. TFM is a complex area of research involving the disciplines of operations research, guidance and control, human factors, and software engineering. Hundreds of human operators make TFM decisions that involve tens of thousands of aircraft, en route air traffic control centers, the Federal Aviation Administration's System Command Center, and many airline operation centers. This paper provides an overview of how TFM decisions are made today and challenges facing the system in the future, and reviews modeling and optimization approaches for facilitating system-wide modeling, performance assessments, and system-level optimization of the national airspace system in the presence of both en route and airport capacity constraints.

I See What You're Thinking: Using Cognitive Models to Represent Working Memory Usage for Traffic Flow Management Decision Support Prototypes

A unique method was developed for explicitly and quantitatively representing working memory in an NGOMSL model (Estes, 2001). Applying this method, MITRE CAASD evaluated alternative designs of a research prototype for TFM ((Air) Traffic Flow Management). The NGOMSL modeling made it possible to quantify the physical workload and working memory benefits of an interface which integrates multiple steps into one window and one thread of interaction, as compared to the prototype's original design. It was found that the integrated design places little burden on working memory, more readily provides information about the task, and allows for less interaction with the interface. This design is planned for use in future TFM research, and some aspects of the design are already planned to be incorporated into operational TFM software.

CRCT (Collaborative Routing Coordination Tools) and TFM (Traffic Flow Management) Decision Support Tool Prototyping

This paper summarizes several years of Traffic Flow Management (TFM) R&D work. The MITRE Corporation's Center for Advanced Aviation System Development (CAASD) has been engaged in a longterm research program to define and refine decision support concepts and capabilities for TFM, the branch of Air Traffic Management that manages the strategic aspect of air traffic by balancing airspace capacity with demand, especially during severe weather or heavy traffic. This paper describes CAASD's work on the concept of Collaborative Routing, in which all affected parties — Federal Aviation Administration (FAA) and aircraft operators - can share in realtime a common picture of TFM problems and potential solutions. The Collaborative Routing Coordination Tools (CRCT) refers to a set of functionalities that support Collaborative Routing, and also to a software platform developed and used by CAASD to conduct the R&D. In this paper, the Collaborative Routing concept is described, and sample user interfaces supporting the concept are presented. Evaluations with operational personnel are described at a high level. Capabilities developed and refined via this work include the FCA (Flow Constrained Area), and the reroute modeling capability. These functions allow the user to evaluate the impact of a rerouting solution to controllers and airspace users (airlines and other pilots), before implementing that solution. Finally, ongoing research on future TFM decision support capabilities is described.

Predictive Models for Air Traffic Arrival Flows at Capacity-Constrained Airports

Traffic flow management (TFM) decision support relies heavily on forecasting of future air traffic demand. Flow managers need to anticipate situations and intervene where appropriate in a proactive rather than reactive mode. The accuracy of the prediction function is of critical importance. The prediction function is accomplished by means of a model, a representation of salient features of the real world. This paper investigates and compares some predictive models. No attempt is made to find or build the ultimate, best predictive model; rather the aim is to explore a framework for comparing models and metrics.The comparison of predictive models of arrival flows was performed by comparing the model currently used by flow managers with several linear regression models. Not knowing in advance what measure of predictive accuracy was best, we selected seven metrics for comparison. Since TFM manages air traffic flows as opposed to individual flights, these metrics generally are oriented toward aggregates of flights. For the comparison, 42 test data sets of flights subject to departure-delay-type flow management for 14 major arrival airports around the country during 1992-1993 were used. An historical database of about 15,000 flights was used for the model building.The selection of best model was not straightforward, since other independent variables, namely performance metric and arrival airport, may also explain differences in predictive accuracy. An analysis of variance (ANOVA) model with the following form was therefore employed to examine the significance of effects:predictive accuracy = f(predictive model, performance metric, arrival airport)The ANOVA model is explicitly a linear function of the (categorical) independent variables. Both main effects and interaction effects were examined.