@techreport{
 title = {Encoder-Decoder Approach to Predict Airport Operational Runway Configuration A case study for Amsterdam Schiphol airport},
 type = {techreport},
 keywords = {Index Terms-runway configuration,machine learning},
 websites = {https://ext.eurocontrol.int/airport},
 id = {a8c01b93-029f-3f62-bea0-2a9d0c8472b0},
 created = {2020-02-02T16:52:28.132Z},
 file_attached = {true},
 profile_id = {da21ad65-ce4d-32fc-9adb-84842d41ea94},
 last_modified = {2020-02-02T16:52:31.204Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The runway configuration of an airport is the combination of runways that are active for arrivals and departures at any time. The runway configuration has a major influence on the capacity of the airport, taxiing times, the occupation of parking stands and taxiways, as well as on the management of traffic in the airspace surrounding the airport. The runway configuration of a given airport may change several times during the day, depending on the weather, air traffic demand and noise abatement rules, among other factors. This paper proposes an encoder-decoder model that is able to predict the future runway configuration sequence of an airport several hours upfront. In contrast to typical rule-based approaches, the proposed model is generic enough to be applied to any airport, since it only requires the past runway configuration history and the forecast traffic demand and weather in the prediction horizon. The performance of the model is assessed for the Amsterdam Schiphol Airport using three years of traffic, weather and runway use data.},
 bibtype = {techreport},
 author = {Dalmau, Ramon and Herrema, Floris}
}

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