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-# Presentations
-
-- ICRAT 2022: [(presentation)](2022_icrat.pdf), demonstration included in the [documentation](https://traffic-viz.github.io/)
-- Opensky Symposium 2021: [(presentation)](2021_tutorial.pdf), demonstration included in the [documentation](https://traffic-viz.github.io/)
-- MOND**AI**s, June 2021: [(venue)](https://datascience.aero/mondais/), [(notebook)](https://raw.githubusercontent.com/traffic-viz/traffic_static/master/notebooks/mondais_2021.ipynb), [(presentation)](2021_mondais.pdf)
-- Opensky Symposium 2020: [(venue)](https://workshop.opensky-network.org/2020), [(paper)](https://doi.org/10.3390/proceedings2020059008), [(notebook)](https://raw.githubusercontent.com/traffic-viz/traffic_static/master/notebooks/opensky_2020.ipynb), [(presentation)](2019_opensky.pdf)
-- Opensky Workshop 2019: [(venue)](https://workshop.opensky-network.org/2019), [(paper)](https://doi.org/10.29007/sf1f), [(notebook)](https://raw.githubusercontent.com/traffic-viz/traffic_static/master/notebooks/opensky_2019.ipynb), [(presentation)](2019_opensky.pdf)
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+---
+title: traffic
+subtitle: a toolbox for processing and analysing air traffic data
+theme: metropolis
+author: Xavier Olive
+aspectratio: 169
+autopandoc: pandoc short.md -t beamer -o short.pdf --pdf-engine=xelatex
+autopandoc-onsave: true
+header-includes:
+  - \metroset{numbering=fraction}
+---
+
+## An open-source library
+
+![](./img/traffic_screenshot.png){ width=250px } \hspace{3cm} ![](./img/qrcode.png){ width=2cm }
+
+- Code: [https://github.com/xoolive/traffic/](https://github.com/xoolive/traffic/)  
+  Documentation: [https://traffic-viz.github.io/](https://traffic-viz.github.io/)
+
+- Development started early 2018
+
+- traffic, a toolbox for processing and analysing air traffic data,  
+  _Journal of Open Source Software_ (4), 2019. DOI: 10.21105/joss.01518
+
+## The researcher's wish list
+
+- **Access to (open) data**  
+  Includes trajectories, flight plans, airspace structure, weather information, etc.
+
+- **Trajectory preprocessing**  
+  Clean and filter data, prepare datasets, enrich with metadata
+
+- **Algorithms**  
+  clustering, detection of operational events, implement KPI
+
+- **Data visualization**  
+  Maps, interactive visualization
+
+## Access to (open) data
+
+- Access to OpenSky Network historical database  
+  [https://github.com/open-aviation/pyopensky](https://github.com/open-aviation/pyopensky)
+
+- Access to description of airspace structure  
+  Parsing facilities for XPlane format, Eurocontrol DDR + AIXM data, FAA open data
+
+- Access to OpenStreetMap data  
+  [https://github.com/xoolive/cartes](https://github.com/xoolive/cartes)
+
+- Access to weather data  
+  METAR history information, ERA5 with [https://github.com/junzis/fastmeteo](https://github.com/junzis/fastmeteo)
+
+**\alert{A tabular \emph{tidy} format}**: pandas, geopandas, xarray
+
+## Access to (open) data [code]
+
+```python
+from traffic.data.samples import *  # for documentation and testing
+from traffic.data.datasets import *  # public datasets included in publications
+
+from traffic.data import airspaces  # public sources or AIRAC data
+from traffic.data import airports  # airports, runways, apron structure
+
+```
+
+![](img/lfpo_layout.pdf){ width=230px }
+
+## Trajectory preprocessing
+
+`pandas` misses a semantics for trajectories  
+`geopandas` suits well geometrical shapes, not time series
+
+There are common noise patterns in data that we learn to process.
+
+\alert{\texttt{traffic} comes up with a semantics for processing trajectory data}
+
+The same semantics applies on:
+
+- individual trajectories (`Flight`) and
+- collections of trajectories (`Traffic`)
+
+## Trajectory preprocessing [code]
+
+```python
+from traffic.core import Flight, Traffic
+
+Flight.from_file(...)  # one single trajectory
+Traffic.from_file(...)  # a collection of Flight
+
+flight.duration  # a pd.Timestamp
+flight.first("10 minutes")  # a new Flight
+flight.intersects(LFBOTMA)  # a boolean
+flight.simplify()  # Douglas-Peucker simplification
+flight.aligned_on_ils("LFPO")  # iterates on segments on final approach
+flight.go_around()
+flight.holding_pattern()
+
+```
+
+## Algorithms
+
+Efficient implementations for common problems:
+
+- **detection of specific events**  
+  holding patterns, go around, point merge, in-flight refuelling, aerial survey, firefighting, etc.
+
+- **trajectory clustering**
+
+- **trajectory generation**
+
+- **closest point of approach**
+
+- **fuel flow estimation**, with OpenAP
+
+- etc.
+
+## Example: select holding patterns and point merge systems
+
+```python
+collection.has("aligned_on_ils('EGLL')").has("holding_pattern").eval()
+```
+
+\centering ![_](img/heathrow_holding.png){ width=200px } \hspace{60pt} ![_](img/london_pointmerge.png){ width=200px }
+
+## Example: compute an occupancy graph for a given airspace
+
+```python
+collection
+.within_bbox(airspaces["LFEE5R"])
+.intersects(airspaces["LFEE5R"])
+.clip(airspaces["LFEE5R"])
+.summary(["callsign", "icao24", "typecode", "start", "stop", "duration"])
+.eval()
+```
+
+## Data visualization
+
+Plotting facilities for all data structures with common visualization solutions:
+
+- Matplotlib
+
+- Leaflet (ipyleaflet)
+  explore trajectories in a widget
+
+- Plotly
+  interactive visualisations
+
+and more exploratory options:
+
+- Mapbox (Open GL)
+
+- JavaScript/Observable options
+
+## Future developments
+
+- Trajectory prediction
+
+- Interaction with state-of-the-art tools
+
+- Improvements on performance
+
+- Facilitate the usage of the same semantics on real-time data
+
+- Exploration of more visualization techniques
+
+**\alert{The 12th OpenSky Symposium}**  
+_7/8 November 2024_, Hamburg, Germany  
+[https://symposium.opensky-network.org/](https://symposium.opensky-network.org/)
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