Dispatcher 2024 – Innaxis.aero

The project

Dispatcher3, an Innovative Action within the frame of CleanSky 2 ITD System, will enhance airlines operations by improving the dispatching and flight operating processes, providing an infrastructure able to lever on historical data and machine learning techniques to systematically estimate the variability between planned and executed flight plans.

Dispatcher3 focuses on the activities prior departure; Pilot3, a CleanSky 2 Innovation Action, focuses on the optimisation of the flight during the execution phase.

Dispatcher3 aims at supporting dispatchers, pilots and the strategic scheduling process:

Dispatchers

Predicting the expected actual performance of flight.
Providing advice on the flight plan design and selection process.
Identifying the precursors of the different variations between planning and execution.

Pilots

Providing information on the expected variance between the flight plan and the execution.
Providing advice on some flight operations.

Schedule planners

Creating the infrastructure needed to store and process flights and operational environment data.
Providing advice on which flights are more prone to variability between schedules and execution blocks.

An introduction to Dispatcher3

Objectives and approach

Final prototype in 1 minute!

Dispatcher3 at glance

Dispatcher3 will be used during the pre-departure but at different decision periods. Independent user-oriented models, which consider the information available at each time-frame, will be developed.

Dispatcher3 is organised in three layers:

Data infrastructure: Powered by DataBeacon (a Multi-sided, open source, data storage and processing platform). Providing private environments, secure data frames, the full-stack AI environment, and a 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 model: consisting on target variable labelling and feature engineering and training, test and validation of machine learning models.
Advice capabilities: producing specific advise to users: dispatchers, pilots and schedule planners.

Development approach

Close coordination with stakeholders and with the Topic Manager (Thales) are key to ensure that Dispatcher3 delivers a suitable solution. An Advisory Board formed by airlines and experts has been set up and will help to steer the project in the right direction. Moreover, counting with Vueling and Skeyes as partners will allow functionalities of Dispatcher3 to be accepted and deploy as soon as possible. Dispatcher3 will maximise synergies with Pilot3 project.

The project will start with the formalisation of the requirements and case studies to be tested. Two prototype versions will be generated, and you will be able to provide feedback to us on the first version on a dedicated workshop!

Public deliverables

16 December 2021 | Innaxis | Dispatcher3

A machine learning model for the prediction of reactionary delay

Author: Paolino De Falco (UoW) Dispatcher3 aims at identifying and analysing the differences between planned and executed flights providing a support for fleet management decisions. One of the main actors involved in the fleet management is the duty manager whose main responsibility is to assess the impact of delays and disruptions preventing their propagation through the network considering a…

30 July 2021 | Innaxis | Dispatcher3

The particularities of Verification and Validation for Machine Learning data driven projects

Authors:Jovana Kuljanin & Julia de Homdedeu (UPC) How does verification and validation approach in machine learning differ from the canonical verification and validation approach? Dispatcher3 is a data-driven project which requires a distinctive approach in the verification and validation. Unlike traditional software development methodologies which typically follow the principal of the ca…

23 July 2021 | Innaxis | Dispatcher3

Identification of data needs for Dispatcher3

Author: Damir Valput (Innaxis) The main objective of Dispatcher3 is to leverage historical data and machine learning techniques to support flight management activities prior to departure, as described in our first blog post. The airline dispatchers assist in flight planning of individual operations considering different data sources such as weather forecast, restrictions in airspace usage and …

29 September 2020 | Innaxis | Dispatcher3

Dispatcher3: Data at the core of flight dispatching

Author: Luis Delgado (UoW) 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….

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 886461.

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