Strategic Traffic Flow Management Research Articles

Fast-time simulation modeling as a tool for identifying best practices in strategic TFM: Initial analysis

Fast-time simulation Traffic Flow Management (TFM) models can play a significant role in helping to identify best practices for weather impact scenarios, to apply these practices, and to review their efficacy. A model that can enforce Traffic Management Initiatives (TMIs) to provide sufficient real ism is highly desirable for these activities. The model must also be comprehensively weather-aware so as to faithfully reflect the effect of convective and non-convective weather on air traffic. We have leveraged the ability of the Dynamic Airspace Routing Tool (DART), a superfast-time simulation model, to objectively evaluate the potential benefits (or added impacts) of alternative TFM strategies in support of operationally relevant what-if testing possibilities. We describe a methodology enabled by these simulation capabilities to objectively, and with great detail, evaluate adverse-weather-related TMI utility and potential alternatives with the aim of building a library of ‘best practices’. This overall approach, developed for visual, operator-driven Strategic TFM analysis through comparison of baseline and alternative scenarios, can also be augmented by a parametric search, i.e. partial optimization, of feasible TMI solutions for still greater testing and automation support for impact management decision-making. We discuss how this simulation model has already been effectively applied to support alternative-response analyses for air traffic operations. The opportunities for Strategic TFM to evolve to a more consistent and efficient operation under this alternative support paradigm are discussed.

Demand modeling for a strategic traffic flow management simulation

Demand modeling for a strategic traffic flow management simulation

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

This paper describes a stochastic analytical model for predicting airport capacity with a look-ahead horizon suitable for strategic traffic flow management. The model extends previous research on airport capacity estimation by explicitly integrating the impact of terminal weather and its uncertainty. Different types of weather forecast inputs are explored, including deterministic forecasts, deterministic forecasts with forecast error models, and ensemble forecasts, to produce distributions of predicted arrival and departure capacity for each runway configuration at an airport. The paper introduces a mathematical capacity prediction model and weather data sources supported by a proof-of-concept prototype implementation, including results of validation studies at Hartsfield–Jackson Atlanta International Airport. These results are compared with standard airport benchmark capacities and actual observed throughputs. Results show that our analytical airport capacity model accurately predicts maximum available airport capacity for Hartsfield–Jackson Atlanta International Airport for different weather conditions. The validation studies reveal a limited impact of forecast uncertainty representation on the accuracy of airport capacity predictions.