CBL - Campus del Baix Llobregat

Projecte llegit

Títol: Taxi time analysis and prediction with ADS-B data. A case study in Barcelona airport.

Estudiants que han llegit aquest projecte:

Director/a: PRATS MENÉNDEZ, XAVIER

Departament: FIS

Títol: Taxi time analysis and prediction with ADS-B data. A case study in Barcelona airport.

Data inici oferta: 27-01-2017     Data finalització oferta: 27-09-2017


Estudis d'assignació del projecte:
    Tipus: Conjunt     Nombre d'estudiants per realitzar-ho: 1-2
     
    Lloc de realització: EETAC
     
    Segon director/a (UPC): MALDONADO DIAZ, OSCAR
     
    Paraules clau:
    taxitime taxi time airport LEBL A-CDM ADS-B
     
    Descripció del contingut i pla d'activitats:
    La idea del treball és modelar el TaxiTime de l'Aeroport de Barcelona
    apartir de dades ADS-B. El treball es dividirà en 3 fases. En la primera
    s'obrindràn dades ADS-B de Barcelona i es descodificaràn. Amb
    aquestes dades es farà un anàlisi estadístic per veure quins són els
    paràmetres que afecten al temps de rodatge. En la segona
    fases'intentarà predir quins seran els diferents temps de rodatge per les
    rutes escollides durant el període d'obres a la 25R de LEBL. La tercera
    fase estarà dedicada a la validació de resultats amb dades ADS-B del
    període d'obres. En aquesta fase caldrà també justificar les possibles
    diferències entre la solució adoptada per el software i la trobada
    experimentalment.
     
    Overview (resum en anglès):
    This document contains a study about taxitime analysis in Barcelona-El Prat airport using ADS-B data. Section 1 shows how to decode ADS-B data and what useful information can be recovered to perform the taxitime analysis. Section 2 shows how to model the airport, including runways, taxiways and stands based on AIP data, satellite images and official maps. Section 3 shows how the positions obtained from ADS-B and the modeled airport can be related to be able to unequivocally define the trajectory that an airplane has followed through this modeled airport. Section 4 shows from all the information compiled in the previous sections, how can be determined the factors that affect taxitime and how to create a model that allows estimating them.

    In previous studies taxitime calculation had been restricted to very specific situations or locations, but in this document, will be tried to relative all parameters (working with speeds, relative queues and differentiation for operations) to be able to extend this calculation to all airport operations. The results show that this goal has been achieved with an accuracy of 2 minutes (A-CDM requirement) of 73$\%$ in departures and 97$\%$ in arrivals.

    The proposed model is not only characterized by high accuracy in static conditions but also shows a good adaptation to changing conditions. Although it's true that the model doesn't work well when the training and evaluating data sets have different conditions, the model has proven to be valid under new conditions with a very small set of training data in the new conditions. Traditional models are based on calculation of point-to-point histories, which need a very large period of data to extract conclusions. With this methods it's difficult to calculate taxitime just after a condition change. With the proposed model, thanks to calculations in velocities and relativities, model is able to extract information from all the available data and create predictions with good accuracy even if the conditions have recently changed.

    This document also presents a real case of calculation in extraordinary conditions where adaptation capacity of the model can be seen in Barcelona-El Prat airport during a one-month runway closure due to maintenance.

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