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Article Dans Une Revue Journal of Open Source Software Année : 2019

Traffic, a toolbox for processing and analysing air traffic data

Résumé

Problems tackled by researchers and data scientists in aviation and air traffic management (ATM) require manipulating large amounts of data representing trajectories, flight parameters and geographical descriptions of the airspace they fly through. Trajectories are mathematical objects used to describe the evolution of a moving object with a finite list of parameters. The most common features in ATM include latitude, longitude, altitude, all indexed by time, with derived ones such as ground speed, track angle and vertical rate. Aircraft dynamic models may expect pitch, roll and yaw angles, together with indicated, computed or true airspeed, Mach number, and more. Further, airspaces are a key element of aviation: they are regulated by specific rules, whereby navigation is allowed to determined types of aircraft meeting strict requirements. Such volumes , assigned to air traffic controllers to ensure the safety of flights and proper separation between aircraft are most commonly described as a combination of extruded polygons. Common operations relevant to trajectories evolving in controlled airspaces range from basic attributes: time of entry, time of exit, duration, maximum or minimum altitudes or speed; to more complex operations like intersections of trajectories with airspaces, distances between pairs of trajectories and more. Top performance and expressivity are key expectations for common tasks like preprocessing and filtering of data, preparation of trajectory datasets, or computation of key performance indicators. The traffic library uses the Python language to reach a large base of academics and data scientists users, to serve and benefit its active community and to build on top of a large catalogue of libraries. Trajectories are modelled on top of Pandas dataframe, a natural solution to represent time series, while airspaces leverage Shapely (Gillies & others, 2007) geometries and operations (intersections, inclusion, cascaded joins among others). traffic provides key operations for analysing trajectories evolving in airspaces. It is particularly useful to programmers and researchers needing to compute statistics, performance indicators and building datasets for common machine learning tasks. Meaningful methods are efficiently written using Pandas and Shapely optimised methods, more obvious operations are directly passed to the underlying dataframes.
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Dates et versions

hal-02294354 , version 1 (23-09-2019)

Identifiants

Citer

Xavier Olive. Traffic, a toolbox for processing and analysing air traffic data. Journal of Open Source Software, 2019, 4 (39), pp.1518, 1-3. ⟨10.21105/joss.01518⟩. ⟨hal-02294354⟩

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