Pilot3: A crew multi-criteria decision support tool – Estimating performance indicators and uncertainty for tactical trajectory management

During a flight, when a change in the operational conditions arises (e.g., new updated weather forecast, delay at reaching a given waypoint), different alternative trajectories can be computed with dedicated optimisation or prediction systems. These systems usually produce trajectories with trade-offs between expected fuel usage and delay. The pilot, or the dispatcher, considers these expected values in order to decide how to tactically operate the aircraft. This approach has two main challenges. Firstly, it requires the translation of arrival delay into parameters which are relevant for the airlines, such as on-time performance and cost of delay. Secondly, uncertainties in the system need to be estimated, such as holding time at arrival, or taxi-in time. Both of these estimations (airlines performance indicators and uncertainty) rely on the airline staff expertise. Finally, the crew faces a multi-criteria decision process as different objectives (cost, on-time performance) and constraints need to be considered. The use of prior to the flight estimations, such as the cost index of the operational flight plan, might not be relevant at the moment of reassessing the flight, as the situation has evolved (for example, the number of passengers who can potentially miss their connections will depend on the status of the fleet of the airline). In other cases, this expected cost of delay could be estimated by the crew or the dispatchers, but generally it is difficult to internalise the dynamics of cost due to IROPS on passengers, or even to estimate the cost of a potential curfew at the end of the day. Uncertainties such as the expected holding delay, distance flown at the arrival TMA, or taxi-in time, might lead to sub-optimal decisions, such as recovering delay, using extra fuel, which does not translate into economic benefit, as larger holding than anticipated might lead to passengers still missing their connection; or shorter distances flown in the TMA means that speed-ups performed during the cruise were unnecessary. Pilot3, a Clean Sky 2 Research and Innovation action, sets out to overcome these issues by developing a multi-criteria support decision tool, which combines explicit estimation of key performance indicators and estimation of ATM operational parameters. These estimators will be developed incrementally, from simple heuristics to machine learning models. Pilot3 prototype comprises five sub-systems: * An Alternatives Generator, which will compute the different alternatives to be considered by the pilot; fed by two independent sub-systems: * Performance Indicators Estimator, which provides the Alternatives Generator with information on how to estimate the impact of each solution for the different performance indicators; * Operational ATM Estimator, which provides the Alternative Generator with information on how to estimate some operational aspects such as tactical route amendments, expected arrival procedure, holding time in terminal airspace, distance flown (or flight time spent) in terminal airspace due to arrival sequencing and merging operations, or taxi-in time; * Performance Assessment Module, which, considering the expected results for each alternative on the different KPIs, is able to filter and rank the alternatives considering airlines and pilots preferences; and * Human Machine Interface, which will present these alternatives to the pilot and allow them to interact with the system. Pilot3 is led by the University of Westminster with the Universitat Politecnica de Catalunya, Innaxis and PACE Aerospace Engineering and Information Technology as partners. The Topic Manager is Thales AVS France SAS. With support from the Advisory Board, Pilot3 has already identified the key operational performance indicators that crew should consider when tactically adjusting their trajectories (on-time performance and total cost, including fuel, IROPs and others); and a literature review and filtering process on multi-criteria decision making techniques has been conducted to select the most suitable method for the different phases of the optimisation process (trajectory generation, filtering and ranking of alternatives)

Dispatcher3 D5.1 - Verification and validation plan

In this deliverable, we present a verification and validation plan designed to carry out all necessary activities along Dispatcher3 prototype development. Given the nature of the project, the deliverable points to a data-centric approach to machine learning that treats training and testing models as an important production asset, together with the algorithm and infrastructure used throughout the development. The verification and validation activities will be presented in the document. The proposed framework will support the incremental development of the prototype based on the principle of iterative development paradigm. The core of the verification and validation approach is structured around three different and inter-related phases including data acquisition and preparation, predictive model development and advisory generator model development which are combined iteratively and in close coordination with the experts from the consortium and the Advisory Board. For each individual phase, a set of verification and validation activities will be performed to maximise the benefits of Dispatcher3. Thus, the methodological framework proposed in this deliverable attempts to address the specificities of the verification and validation approach in the domain of machine learning, as it differs from the canonical approach which are typically based on standardised procedures, and in the domain of the final prospective model. This means that the verification and validation of the machine learning models will also be considered as a part of the model development, since the tailoring and enhancement of the model highly relies on the verification and validation results. The deliverable provides an approach on the definition of preliminary case studies that ensure the flexibility and tractability in their selection through different machine learning model development. The deliverable finally details the organisation and schedule of the internal and external meetings, workshops and dedicated activities along with the specification of the questionnaires, flow-type diagrams and other tool and platforms which aim to facilitate the validation assessments with special focus on the predictive and prospective models

Dispatcher3 D1.1 - Technical resources and problem definition

This deliverable starts with the proposal of Dispatcher3 and incorporates the development produced during the first five months of the project: activities on different workpackages, interaction with Topic Manager and Project Officer, and input received during the first Advisory Board meeting and follow up consultations. This deliverable presents the definition of Dispatcher3 concept and methodology. It includes the high level the requirements of the prototype, preliminary data requirements, preliminary technical infrastructure requirements, preliminary data processing and analytic techniques identification and a preliminary definition of scenarios. The deliverable aims at defining the view of the consortium on the project at these early stages, incorporating the feedback obtained from the Advisory Board and highlighting the further activities required to define some of the aspects of the project

Modus D4.1 Interface to modal choice model

This deliverable is the first deliverable of WP4 of the Modus project which aims to develop highly detailed low-level results on the present and future of the mobility of passengers in Europe based on flight and passenger metrics. The purpose of this document is to describe the methodology designed and developed to translate the output results of the modal choice model into individual passenger itineraries that are going to be used by the mobility models. Additionally, it outline so-far identified data requirements and processing needs to create valid input for the rest of the models developed in Modus: flight-centred airside model RNEST, passenger-centric airside model Mercury, and the landside model (i.e. door-to-door model)

Dispatcher3: Innovative processing for flight practices

An experienced dispatcher will have a good understanding on the differences between planned and executed flight plans. These differences will be driven by uncertainty factors such as, which runway is the one that will be used at arrival, what is the actual weather that the flight will experience, or how much delay will the flight experience as holding at the arrival. Besides safety aspects, dispatchers will consider, among other parameters, the operational environment and constraints (such as flight date and time, network congestion or route availability), the situation of the airline fleet (e.g., delays), airline policies and performance indicators (cost and on-time performance) to select the most suitable flight plan: route, profile and cost index. Some of these aspects can be automatised by using advanced flight dispatching and planning tools, but having a good understanding of the expected discrepancies between planned and realised, and of the key driving factors for these variations is key to produce robust and efficient solutions. Dispatcher3, an Innovative Action within the frame of CleanSky 2 ITD System, will provide a data infrastructure for levering on historical data and machine learning techniques to systematically estimate the variability between planned and executed flight plans. The project is led by the University of Westminster, with Innaxis, the Universitat Politecnica de Catalunya, Vueling Airlines, PACE Aerospace Engineering and Information Technology and skeyes as partners. The Topic Manager is Thales AVS France SAS. Dispatcher3 focuses on the activities prior to departure and aims at supporting dispatchers, pilots and the strategic scheduling process. * Dispatchers will benefit from * predictions of the expected actual performance of a flight, * advice on the flight plan design and selection process, and * identification of the key driving factors for the variations between planning and execution. * The flight crew will obtain * information on the expected variance between the flight plan and the flight execution, and * qualitative advice on some flight operations. * Schedule planners will count with an infrastructure able to identify which flights are systematically prone to variations between schedules and execution blocks requiring the need of further assessment. Dispatcher3 is organised in three layers: * Data infrastructure: Powered by DataBeacon (a multi-sided, open source, data storage and processing platform). It provides private environments to perform analytical and modelling tasks, secure data fusion, and a cloud computing scalable infrastructure; * Predictive capabilities; with two different modules: * Data acquisition and preparation: with a first phase of data wrangling and a second step of descriptive analytics. * Predictive modelling: following the standard machine learning pipeline of target variable labelling, feature engineering, and finally training, testing and validation of machine learning models. * Advice capabilities: relying on the output of the predictive layer and producing specific advise to users: dispatchers, pilots and schedule planners. Dispatcher3 will consider datasets available within airlines, but also analyse which datasets are currently not accessible but could benefit these predictive capabilities. This quantification on the predictive improvement will help identify which multi-stakeholders collaborations should be established

Domino D5.3 Final tool and model description, and case studies results

This deliverable presents the final results obtained from the Domino project. It presents the corresponding metrics, the model, and a detailed analysis of two case studies. The main modifications to the model with respect to the previous version are highlighted, including curfew management. The calibration of the model is presented, which is similar to the previous version, with more in-depth analyses and further effort dedicated to the calibration process. Two case studies are defined in this deliverable, using previous definitions of the three base mechanisms: 4D trajectory adjustments, flight prioritisation, and flight arrival coordination. The case studies are defined to have a focused insight into the efficiency of the mechanisms in specific environments. The two case studies are run by the model and analysed using metrics previously defined, including centrality and causality metrics. The results show different levels of efficiency for the three mechanisms, highlight the degree of robustness to the propagation of negative effects (such as delay) in the system, demonstrate various trade-offs between the indicators, and support a discussion of the limit of the mechanisms

Domino D3.1 - Architecture definition

This deliverable presents the concept of operation of Domino. It includes a description of the systems, subsystems and processes that will be taken into account in the model, as well as the general scope of the model. For each of the mechanisms suggested to be modelled in the project, the deliverable provides a set of possible operational concepts and uptake/scope to be deployed

Domino D5.2 - Investigative case studies results

This deliverable presents the results from the analysis of the model executing the investigative case studies. The document focuses on the validation activities and the results for the three mechanisms modelled in Domino in the unitary case studies. The three mechanism are: 4D Trajectory Adjustment, which focuses on the use of dynamic cost indexing and wait-for-passengers rules; Flight Prioritisation, which considers the possibility of slot swapping at ATFM regulations; and Flight Arrival Coordination, which models different optimisation approaches E-AMAN could consider. Each mechanism has three levels of implementation: Level 0 (with current capabilities), Level 1 (with more advanced features) and Level 2 (more explorative). The traffic is set on a given day (12 September 2014) considering flights and passengers’ itineraries. Two levels of delay are considered: default and stressed. In total 14 scenarios have been modelled and analysed. This deliverable presents the use of classical and network metrics (centrality and causality) on the outcome of the whole European level agent-based model. The model still requires further development and adjustment, but results show that it is already capable of capturing complex interactions among the ATM elements. Finally, the network metrics are already presenting their potential to capture non-direct interactions between elements in the system. The results have been shared with experts and airspace users at two workshops. The feedback obtained and the results of the analysis and validation activities will be considered for the final version of Domino

Domino D3.3 - Adaptive case studies description

This deliverable presents the improvement planned to be performed until the end of the project regarding the model (implementation changes, recalibration and the simulation outputs), plus the metrics and scenarios that will be re-run with the model. These changes are based on the insights gathered through the analysis activities performed in the scope of investigative case studies (see D3.2 Investigative case studies description and D5.2 Investigative case studies results) and the feedback obtained from experts and stakeholders on the different workshops activities performed (see D6.3 Workshop results summary). These insights highlighted missing features of the model and potential improvements, as well as some gaps and shortcomings. The scenarios for this analysis have been chosen highly selectively in order to prioritise the depth of the analysis and methodology development over a large number of scenarios, as these have already been analysed in the scope of the investigative case studies

Pilot3 D5.2 - Verification and validation report

The deliverable provides the outcomes from the verification and validation activities carried during the course of work package 5 of the Pilot3 project, and according to the verification and validation plan defined in deliverable D5.1 (Pilot3 Consortium, 2020c). Firstly, it presents the main results of the verification activities performed during the development and testing of the different software versions. Then, this deliverable reports on the results of internal and external validation activities, which aimed to demonstrate the operational benefit of the Pilot3 tool, assessing the research questions and hypothesis that were defined at the beginning of the project. The Agile principle adopted in the project accompanying with the five five-level hierarchy approach on the definition of scenarios and case studies enabled the flexibility and tractability in the selection of experiments through different versions of prototype development. As a result of this iterative development of the tool, some of the research questions initially defined have been revisited to better reflect the validation results. The deliverable also reports the feedback received from the experts during the internal and external meetings, workshops and dedicated (on-line) site visits. During the validation campaign, both subjective qualitative information and objective quantitative data were collected and analysed to assess the Pilot3 tool. The document also summarises the results of the survey that were distributed to the external experts to assess the human-machine interface (HMI) mock-up developed in the project