Periodic Reporting for period 4 - COPTRA (COmbining Probable TRAjectories)
Periodo di rendicontazione: 2017-09-01 al 2018-02-28
The prediction of future air traffic situations is central to ATC planning. Its uses range from Air Traffic Controller workload management to ensure capacity, to helping the setup flow management measures in case the demand cannot be accommodated.
Different methods are used to generate forecasts for different time horizons to support relevant decisions; the basic question always being ‘how can the demand be met?’ Long-term answers might be ‘build a new control centre’, ‘train some new controllers’ and so on. The medium-term question might be answered by managing the controller leave roster and planning the ‘sector opening scheme’. The time-frame of main concern of this work is the ATC planning horizon (about 90 mins), with a mix of activated airborne flights (RBT available) and flights still in planning (SBT available). It is however intended that the scheme developed in this work will be generally applicable to all time-frames.
Trajectory Based Operations brings together many different improvements that allow the uncertainty of trajectory prediction to be better managed and reduced. These include downlinking of the Extended Projected Profile from the flight to enrich ground trajectory predictions, sharing of detailed information on the ground through SWIM (IOP), the submission of more detailed flight plan information, the use of 4D contracts during the flight, increasing adoption of Airport CDM, and so on.
For a given location, a prediction of the traffic that will cross it at some moment in the future is a mix of trajectories of flights which are airborne, flights that have been filed but not yet taken off and flight plans that have not yet been filed. Each type of flight has a different level of uncertainty or level of inaccuracy with which the prediction can be made. Current systems and operational processes mostly rely on human judgement and experience to deal with this mix of uncertainty. The result is often that a very imprecise balance is struck between demand and capacity leading to capacity going unused or significant last minute adjustments needing to be made.
In this context, the main concepts defined, modelled and studied by COPTRA are the notions of probabilistic trajectories and traffic situations. The central idea researched is to develop new methods to build the probabilistic traffic prediction by combining the probabilistic trajectories.
Building on the considerable inter-disciplinary expertise in trajectory prediction, applied mathematics and ATC planning accumulated by the project partners, COPTRA proposes an operational concept where the uncertainty of the predicted trajectories (hopefully reduced in TBO) is made explicit at trajectory prediction level and combined using state of the art applied mathematics methods to build a probabilistic traffic situation (i.e. traffic situation where the uncertainty is identified and specifically accounted for). These probabilistic traffic situations will be used to improve the prediction of occupancy counts used in ATC Planning and convey better information to the human operator.
Different methods are used to generate forecasts for different time horizons to support relevant decisions; the basic question always being ‘how can the demand be met?’ Long-term answers might be ‘build a new control centre’, ‘train some new controllers’ and so on. The medium-term question might be answered by managing the controller leave roster and planning the ‘sector opening scheme’. The time-frame of main concern of this work is the ATC planning horizon (about 90 mins), with a mix of activated airborne flights (RBT available) and flights still in planning (SBT available). It is however intended that the scheme developed in this work will be generally applicable to all time-frames.
Trajectory Based Operations brings together many different improvements that allow the uncertainty of trajectory prediction to be better managed and reduced. These include downlinking of the Extended Projected Profile from the flight to enrich ground trajectory predictions, sharing of detailed information on the ground through SWIM (IOP), the submission of more detailed flight plan information, the use of 4D contracts during the flight, increasing adoption of Airport CDM, and so on.
For a given location, a prediction of the traffic that will cross it at some moment in the future is a mix of trajectories of flights which are airborne, flights that have been filed but not yet taken off and flight plans that have not yet been filed. Each type of flight has a different level of uncertainty or level of inaccuracy with which the prediction can be made. Current systems and operational processes mostly rely on human judgement and experience to deal with this mix of uncertainty. The result is often that a very imprecise balance is struck between demand and capacity leading to capacity going unused or significant last minute adjustments needing to be made.
In this context, the main concepts defined, modelled and studied by COPTRA are the notions of probabilistic trajectories and traffic situations. The central idea researched is to develop new methods to build the probabilistic traffic prediction by combining the probabilistic trajectories.
Building on the considerable inter-disciplinary expertise in trajectory prediction, applied mathematics and ATC planning accumulated by the project partners, COPTRA proposes an operational concept where the uncertainty of the predicted trajectories (hopefully reduced in TBO) is made explicit at trajectory prediction level and combined using state of the art applied mathematics methods to build a probabilistic traffic situation (i.e. traffic situation where the uncertainty is identified and specifically accounted for). These probabilistic traffic situations will be used to improve the prediction of occupancy counts used in ATC Planning and convey better information to the human operator.
1. Building probabilistic trajectories (WP02)
WP02 focuses on the individual trajectory and on how error is created and propagated. Main activities executed throughout the referred period include the development of the framework used to process the data required by the stochastic trajectory predictor. The work also has included the refinement of the stochastic trajectory predicto as well as the characterisation of the different flight trajectory uncertainty sources.
2. Combining Probabilistic Trajectories (WP03)
WP03 mainly focuses on combining probabilistic trajectories to identify their impacts on traffic flows. Previous work dealt with the quantification of uncertainty at the sector level based on uncertainties at the trajectory level. The work performed the current reporting period has focused on the issues happening upstream, namely reactionary delays. An approach based on both on theoretical development and on the design and implementation of two algorithms focusing on the prediction of uncertainty on the flight take-off time due to precedence. Additionally, WP03 has completed the Queue Network Model tool enabling to assess uncertainty and delay propagation over whole European Air Traffic Network. In addition to the previous work, WP04 has investigated different conceptual models to define/understand properties of the airspaces/airports such as stability of the network or dynamic resiliency. In particular, WP04 has elaborated a model on epidemic modelling .
3. Application of Probabilistic Traffic Prediction to ATC Planning (WP04)
WP04 elaborates a set of benefit mechanisms and associated use cases and scenarios that describe how the approach proposed by COPTRA can be exploited operationally. This WP also deals with the adaptation of one of the existing DCB prototype validation tools (NetPerf or TESEO) to measure the improvements. The work performed has focused on the elaboration of the validation plan and on the design and implementation of three validation exercises aiming to establish the operational validity of the proposed COPTRA algorithms and toolset.
WP02 focuses on the individual trajectory and on how error is created and propagated. Main activities executed throughout the referred period include the development of the framework used to process the data required by the stochastic trajectory predictor. The work also has included the refinement of the stochastic trajectory predicto as well as the characterisation of the different flight trajectory uncertainty sources.
2. Combining Probabilistic Trajectories (WP03)
WP03 mainly focuses on combining probabilistic trajectories to identify their impacts on traffic flows. Previous work dealt with the quantification of uncertainty at the sector level based on uncertainties at the trajectory level. The work performed the current reporting period has focused on the issues happening upstream, namely reactionary delays. An approach based on both on theoretical development and on the design and implementation of two algorithms focusing on the prediction of uncertainty on the flight take-off time due to precedence. Additionally, WP03 has completed the Queue Network Model tool enabling to assess uncertainty and delay propagation over whole European Air Traffic Network. In addition to the previous work, WP04 has investigated different conceptual models to define/understand properties of the airspaces/airports such as stability of the network or dynamic resiliency. In particular, WP04 has elaborated a model on epidemic modelling .
3. Application of Probabilistic Traffic Prediction to ATC Planning (WP04)
WP04 elaborates a set of benefit mechanisms and associated use cases and scenarios that describe how the approach proposed by COPTRA can be exploited operationally. This WP also deals with the adaptation of one of the existing DCB prototype validation tools (NetPerf or TESEO) to measure the improvements. The work performed has focused on the elaboration of the validation plan and on the design and implementation of three validation exercises aiming to establish the operational validity of the proposed COPTRA algorithms and toolset.
1. Building probabilistic trajectories
Development of a stochastic trajectory predictor that instantiates the proposed Polynomial Chaos framework to cope with trajectory prediction uncertainties
2. Combining probabilistic trajectories in probabilistic traffic forecasts
Queue Network Model (QNM) enables the identification of stochastic parametrisation of the ATM network as to be able to predict the behaviour of the network under disturbances. The QNM studies the required granularity on network model, the Traffic flow propagation, the Topological sector transition queues and air traffic/sector complexity. A complete framework for developing Operations Research tools for enhancing Air Traffic across the European Sky. This takes the form of a Weighted Graph and a mathematical model.
3. Application of probabilistic traffic prediction to ATC planning
Two benefit mechanisms have been identified. These mechanisms are supported by operational use cases. The graph models and analysis proposed offer a better vision and understanding of uncertainty at the network level. The identification of critical flights will allow for a better choice when rerouting planes.
Development of a stochastic trajectory predictor that instantiates the proposed Polynomial Chaos framework to cope with trajectory prediction uncertainties
2. Combining probabilistic trajectories in probabilistic traffic forecasts
Queue Network Model (QNM) enables the identification of stochastic parametrisation of the ATM network as to be able to predict the behaviour of the network under disturbances. The QNM studies the required granularity on network model, the Traffic flow propagation, the Topological sector transition queues and air traffic/sector complexity. A complete framework for developing Operations Research tools for enhancing Air Traffic across the European Sky. This takes the form of a Weighted Graph and a mathematical model.
3. Application of probabilistic traffic prediction to ATC planning
Two benefit mechanisms have been identified. These mechanisms are supported by operational use cases. The graph models and analysis proposed offer a better vision and understanding of uncertainty at the network level. The identification of critical flights will allow for a better choice when rerouting planes.