Pilot3 D1.1 - Technical resources and problem definition

This deliverable starts with the proposal of Pilot3 but incorporates the development produced during the first four months of the project: activities on different workpackages, interaction with Topic Manager and Project Officer, and input received during the first Advisory Board meeting. This deliverable presents the definition of Pilot3 concept and methodology. It includes the high level the requirements of the prototype, preliminary data requirements, preliminary indicators that will be considered and a preliminary definition of case studies. The deliverable aims at defining the view of the consortium on the project at these early stages, while highlighting the feedback obtained from the Advisory Board and the further activities required to define some of the aspects of the project

A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem

This research has been carried out as part of the PhD research project funded by Technical University of Denmark and Banedanmark company which is responsible for the operation and maintenance of the Danish railway network. This work has been partially funded by the DAASE project, EPSRC programme grant EP/J017515/1

Airlift scheduling for the upgraded command and control system of military airlift command.

April 1984This report describes a conceptual design for automation of the scheduling of airlift activities as part of the current upgrade of the MAC C2 System. It defines the airlift scheduling problem in generic terms before reviewing the current procedures used by MAC; and then a new scheduling system aimed at handling a very busy and dynamic wartime scenario, is introduced. The new system proposes "Airlift Scheduling Workstations" where MAC Airlift Schedulers would be able to manipulate symbolic information on a computer display to create and quickly modify schedules for aircraft, crews, and stations. For certain sub-problems in generating schedules, automated decision support algorithms would be used interactively to speed the search for feasible and efficient solutions. Airlift Scheduling Workstations are proposed to exist at each "Scheduling Cell", a conceptual organizational unit which has been given sole and complete responsibility for developing the schedule of activities for a specific set of airlift resources-aircraft by tail number, aircrew by name, and stations by location. A Mission Scheduling Database is located at each cell to support the Airlift Scheduling Workstation, and requires information communicated by Airlift Task Planners, and, Airlift Operators at many other locations. These locations would have smaller workstations with local databases, and database management software to assist Task Planners and Operators in viewing current committed and planned schedule information of particular interest to them, and to allow them to send information to the Mission Scheduling Database. The Command and Control processes for Airlift have been structured into a three level hierarchy in this report: Task Planning, Mission Scheduling, and Schedule Execution. Task Planners deal with Airlift Users and Mission Schedulers, but not Airlift Operators. Task Planning has three sub-processes: Processing User Requests; Assigning Requirements and Resources; and Monitoring Task Status. Task planning does not create missions, schedule the missions, or route aircraft. Mission Schedulers deal with Task Planners and Airlift Operators, but not Airlift Users. Mission Scheduling combines several sub-processes to allow efficient schedules to be quickly generated at the ASW (Airlift Scheduling Workstation). These sub-processes are: Mission Generation, Schedule Map Generation (for each type of aircraft), Crew Mission Sequence Generation, Station Schedule Generation, Management of Schedule Status, and Monitoring Schedule Execution and Resource Status. It is important that all these processes be co-located and processed by the Airlift Scheduling Cell. Schedule Execution is performed by Airlift Operators assigned by the scheduling process. It has three sub-processes: Monitor Assigned Schedules, Report Resources Assigned to Schedule, Report Local Capability Status. The assignment of local resources such as aircraft by tail, and crew by name is actually another scheduling process, but has not been studied in this report. Airlift Operators do not deal with Task Planners, but may deal with Airlift Users to finalize details of the scheduled operations. This three level hierarchy is compatible with the current organizational structures of MAC Command and Control. However, it is clear that both the current organizational structures and procedures of MAC Command and Control for both tactical and strategic airlift will be significantly affected by the introduction of the automated scheduling systems envisioned here. These changes will occur in an evolutionary manner after the upgraded MAC C2 system is introduced.Prepared for the Electronic Systems Division, Air Force Systems Command, USAF, Hanscom Air Force Base, Bedford, M

On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos

Dispatcher3 – Machine learning for efficient flight planning - Approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decision making processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general

Dispatcher3 – Machine learning for efficient flight planning: approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decisionmaking processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general.This work is performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The Topic Manager is Thales AVS France SAS. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein.Postprint (published version

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

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