TESA: Trajectory prediction and conflict resolution for Enroute-to-enroute Seamless Air Traffic management.

Imperial College London



The current tactical approach to Air Traffic Management (ATM) is unable to meet future capacity, safety and environmental demands. A new strategic, collaborative and automated approach to ATM is required worldwide. A key aspect of collaboration and automation is the need to guarantee common situational awareness between all stakeholders as a function of time, extrapolated into the future. This is achieved through accurate and reliable Trajectory Prediction (TP). The first objective of TESA will be to extend its current high-performance en-route model to the remaining phases of operation and develop real-time uncertainty models, while the second will use the advanced TP model developed in the first part of the project together with aircraft intent, to develop a model for optimal and strategic resolution of conflicts. TESA will validate the TP models with real operational aircraft data and perform a sensitivity analysis of the input model uncertainties with respect to the reliability of TP and CDR.


Introduction and problem statement


Due to the rapid increase in air travel, there is an urgent need to increase end-to-end airspace capacity and improve safety. Congestion with its consequences in terms of delays and safety risk are significant concerns in high air traffic density areas, such as terminal areas and airports. A number of factors contribute towards this saturation: lack of strategic coordination between aircraft and ATC, inadequate airport infrastructure, and inefficient airspace structures. Currently, there is limited sharing of information (collaborative decision making) between the various elements, with many decisions taken in isolation and at a local level. This leads to inefficient flight-profiles. A key aspect of addressing the resulting capacity problems is to enhance common situational awareness between all aircraft and ATC, not only on a snapshot basis but as a function of time extrapolated into the future. Moreover, the limited situational awareness by pilots of surrounding traffic, limits their pro-active approach within the ATM process.

Currently aircraft operate essentially on 3D flight plans (in the air) and 2D plans (on the ground), with limited advance planning, aircraft being given priority on a first-come first-served basis. This leads to aircraft out-of-slot being served before those in-slot if the out-of-slot aircraft arrives first. The lack of highly accurate and reliable planning capability has resulted in a lack of efficient and reliable standard optimized arrival- or departure sequencing tools. On the ground, the lack of advance planning leads to poor predictability of the taxi process, and the non-integration of the turn-around process leads to non-optimal overall planning, as well as insufficient runway incursion prevention measures. Furthermore, poor departure and arrival sequencing tools and the non-integration of these tools with surface movement tools lead to incorrect assumptions in the planned runway capacity and unnecessary high separations between aircraft (i.e. inefficiencies in runway usage). This leads to domino effects in delays of other aircraft, increasing airborne and ground holdings. These primary shortcomings lead to secondary shortcomings, such as non-optimal flight paths (i.e. horizontal and vertical flight inefficiencies), resulting in delays, increases in fuel consumption, and increased impacts on the environment. Overall, the current ATM system is thus far from optimal, unable to perform advance planning to maximise airport and TMA capacity. With the tools and procedures in use today the increase of capacity will therefore, be fundamentally limited and is already reaching its limits. Moreover, the limit imposed on airports by inadequate infrastructure, and environmental and political considerations, is a key driver of overall airspace capacity. This adds to a number of operational shortcomings that contribute to the creation of capacity bottlenecks in the terminal areas and at airports. Further limitations identified in SESAR of the present system are the fixed volume and route structures, which are fragmented, preventing aircraft from flying their optimal trajectory and creating unnecessary additional workload for controllers.


Project objectives and expected results


The aim of this project is to address current capacity shortages of air travel in European airspace by increasing the overall level of automation of ATM, whilst maintaining or enhancing safety and minimising the impacts of aviation on the environment. In this respect, this project will contribute to the development of the elements of an ATM system that is increasingly strategic and where the role of the human operator will increasingly shift towards the employment of automated decision support tools. Therefore, the first concrete objective is to develop a trajectory prediction tool with the necessary integrity to provide the necessary confidence to controllers and pilots for their use in a real environment. The proposed development will focus on understanding and modelling TP uncertainties. This in turn will provide the basis for the development of a CDR decision logic which simultaneously optimises safety and efficiency. Currently, CDR tools are limited in performance. This is because a significantly large safety margin needs to be allocated to any resolution manoeuvre in order to compensate for the lack of knowledge of the TP uncertainties. By better understanding these uncertainties, the conflict resolution decision logic can be rendered more efficient, the second key objective of this project.


Approach / methodology


To achieve this vision, the TESA project aims to address issues that currently limit the use of trajectory prediction and conflict detection and resolution in improving air travel safety and capacity. The following approach will be used:

  • Develop advanced realistic trajectory prediction algorithms under nominal operations, addressing the key limitations of current models of aircraft dynamics and the FMS.
  • Develop TP uncertainty models, placing special emphasis on developing a reliable estimation methodology of TP integrity.
  • Develop novel advanced conflict detection models, based on the developed TP uncertainty models.
  • Develop novel advanced conflict resolution tools, optimising safety, efficiency and capacity, based on the developed conflict detection and TP tools.
  • Validate the above tools by means of advanced simulations as well as extensive real aircraft flight and taxiing data.