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In the aspect of Trajectory Based Operations (TBO), accurate trajectory predictions are essential for meeting future concepts defined in SESAR and NextGen visions. The 4D trajectories are results of generated flight plans by Airline Operation Centers to be executed by pilots and monitored by Air Traffic Controllers. Trajectory generation is a process with many input sources such as flight route, speed schedule, initial conditions and environmental conditions. However, the uncertainty in these states generates discrepancies between planned and actual trajectories. This paper distinctly investigates the wind uncertainty by analyzing the forecast data in airline’s operational flight plans and actual wind computed by aircraft’s onboard systems (i.e., Quick Access Recorder). The study goes through the methodology of speed and altitude optimization for cruise phase. The simulation studies reveal the impact of wind uncertainty and potential fuel saving benefits when the precise wind data is available. The case studies are based on historical planned and flown flights.

Considering the transformation in roles of existing air traffic management technologies, future flight operations and flight deck systems will need additional avionics and operational procedures that involve adaptive algorithms and advanced decision support tools. The main purpose of this article is to provide a theoretical framework for tactical 4D-trajectory planning and conflict resolution of an aircraft equipped with novel automation tools. The proposed 4D-trajectory-planning method uses recent algorithmic advances in both probabilistic and deterministic methods to fully benefit from both approaches. We have constructed an aircraft performance model based on Base of Aircraft Data 4 with high-level hybrid flight template automatons and low-level flight maneuver automatons. This multi-modal flight trajectory approach is utilized to generate cost-efficient local trajectory segments instead of solving complex trajectory-generation problems globally. The proposed sampling-based trajectory planning algorithm spatially explores the airspace and provides proper separation through local trajectory segments and guarantees asymptotic optimality under certain conditions. Moreover, we have integrated the cross-entropy method, which transforms the sampling problem into a stochastic optimization problem, rapidly converges on the minimum cost trajectory sequence by utilizing available flight plans, and reduces the amount of sampling. The integration of the proposed strategies lets us solve challenging, real-time in-tactical 4D-trajectory planning problems within the current and the envisioned future realm of air traffic management systems.

Aircraft trajectory prediction refers to an estimate of the future positions of a flight for a given look ahead time. In the context of achieving safe and seamless traffic management, efficient and modular trajectory prediction tools will be the crucial functions in future trajectory based operations. The main objective of this paper is to present a highly scalable trajectory generation infrastructure (TGI) that is capable of predicting 4D trajectories for given initial conditions and flight plans, detecting conflicts and providing separation maneuvers, avoiding restricted regions declared as NOTAM, calculating air sector entry/exit and occupancy times. The proposed TGI includes advanced aircraft performance models based on BADA (Base of Aircraft Data), standard navigation functions, standardized interface for operational rules of airspace inputs (i.e. airports, navaids, fixes, Standard Instrument Departure Route (SID), Standard Arrival Route (STAR) procedures), trajectory control algorithms, conflict detection functions and conflict resolution action library. The developed tool provides a comprehensive platform for air traffic control research, and the its functions are explained throughout this paper.

Efficient trajectory prediction tools will be the crucial functions in future trajectory-based operations (TBO). In addition to win and controller actions, uncertainties in climbing flights are major components of prediction errors in a flight trajectory. Due to the operational concerns, aircraft take-off weight and climb speed intent, which are key performance parameters that define climb profiles, is not entirely available to round-based trajectory prediction infrastructure. In the scope of air traffic flow management, sector entry and exit times, including where the climb ends and descending starts, are the main inputs for demand- capacity balancing processes. In this work, we have focused on uncertainties over climb trajectory to quantify and analyze their impact on climb times to cruise altitudes. We have used model-driven data statistical approaches through aircraft flight record data sets (i.e. QAR). As result of this analyze, probabilistic definitions are generated for aircraft take-off weight and speed intent. The regression between these climb parameters and flight distance is acquired to reduce the uncertainty at strategical level. Moreover, reducing climb uncertainty through adaptive uncertainty reduction is also demonstrated at the tactical level of flight. Through the simulations, the impact of reducing the uncertainty in aircraft mass on climb time is illustrated.