English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Air traffic is increasing world wide at a steady annual rate, and airport congestion is already a major issue for air traffic managers. This paper presents a model based on neural networks to predict the position of aircraft on the air- port, during landing or takeoff. The same model can also be used to predict the behavior of other vehicles moving on the airport. The predictions help to detect near-collision situations earlier, giving air traffic controllers additional time to take remedial actions. The system uses the list of coordinates produced by the airport radar system, and obtains a prediction of the future position of each object. It is only necessary to store a short history of positions for each object in order to per- form the estimation. This estimation has an average error comparable to the size of the airplane when the algorithm is ad- justed for 20 second look ahead. The proposed model has been evaluated using data from Chicago O'Hare International Airport, which is the airport with the highest number of movements (from 2001 to 2004).

Computing Aircraft Position Prediction~!2008-08-05~!2008-10-18~!2008-11-28~!