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2 results for “Go-Around”
Large Landing Trajectory Data Set for Go-Around Analysis
<p>Large go-around, also referred to as missed approach, data set. The data set is in support of <a href="https://www.mdpi.com/2673-4591/28/1/2">the paper</a> presented at the OpenSky Symposium on November the 10th.</p> <p>If you use this data for a scientific publication, please consider citing <a href="https://doi.org/10.3390/engproc2022028002">our paper</a>.</p> <p>The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from <a href="https://opensky-network.org/data/impala">OpenSky Network's historical data base</a> for the year 2019. The published data set contains multiple files:</p> <p><strong>go_arounds_minimal.csv.gz</strong></p> <p>Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> </tbody> </table> <p>The last two columns, <em>n_approaches</em> and <em>n_rwy_approached</em>, are useful to filter out training and calibration flight. These have usually a large number of <em>n_approaches</em>, so an easy way to exclude them is to filter by <em>n_approaches > 2.</em></p> <p><strong>go_arounds_augmented.csv.gz</strong></p> <p>Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> <tr> <td>registration</td> <td>string</td> <td>Aircraft registration</td> </tr> <tr> <td>typecode</td> <td>string</td> <td>Aircraft ICAO typecode</td> </tr> <tr> <td>icaoaircrafttype</td> <td>string</td> <td>ICAO aircraft type</td> </tr> <tr> <td>wtc</td> <td>string</td> <td>ICAO wake turbulence category</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td> <p>string</p> </td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometre</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>operator_country</td> <td>string</td> <td>ISO Alpha-3 country code of the operator</td> </tr> <tr> <td>operator_region</td> <td>string</td> <td>Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>wind_speed_knts</td> <td>integer</td> <td>METAR, surface wind speed in knots</td> </tr> <tr> <td>wind_dir_deg</td> <td>integer</td> <td>METAR, surface wind direction in degrees</td> </tr> <tr> <td>wind_gust_knts</td> <td>integer</td> <td>METAR, surface wind gust speed in knots</td> </tr> <tr> <td>visibility_m</td> <td>float</td> <td>METAR, visibility in m</td> </tr> <tr> <td>temperature_deg</td> <td>integer</td> <td>METAR, temperature in degrees Celsius</td> </tr> <tr> <td>press_sea_level_p</td> <td>float</td> <td>METAR, sea level pressure in hPa</td> </tr> <tr> <td>press_p</td> <td>float</td> <td>METAR, QNH in hPA</td> </tr> <tr> <td>weather_intensity</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - intensity</td> </tr> <tr> <td>weather_precipitation</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - precipitation</td> </tr> <tr> <td>weather_desc</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - descriptor</td> </tr> <tr> <td>weather_obscuration</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - obscuration</td> </tr> <tr> <td>weather_other</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - other</td> </tr> </tbody> </table> <p>This data set is augmented with data from various public data sources. Aircraft related data is mostly from the <a href="https://opensky-network.org/aircraft-database">OpenSky Network's aircraft data base</a>, the METAR information is from the <a href="https://mesonet.agron.iastate.edu/info/datasets/metar.html">Iowa State University</a>, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the <a href="https://library.wmo.int/doc_num.php?explnum_id=11220">WMO's Aerodrom Reports and Forecasts handbook</a>.</p> <p><strong>go_arounds_agg.csv.gz</strong></p> <p>Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>n_landings</td> <td>integer</td> <td>Total number of landings observed on this runway in 2019</td> </tr> <tr> <td>ga_rate</td> <td>float</td> <td>Go-around rate, per 1000 landings</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td>string</td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometres</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> </tbody> </table> <p>This aggregated data set is used in the paper for the generalized linear regression model.</p> <p><strong>Downloading the trajectories</strong></p> <p>Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the <a href="https://github.com/xoolive/traffic">Traffic</a> library for easy access to the database:</p> <pre><code class="language-python">import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic # load minimum data set df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"]) # select London City Airport, go-arounds, and 2019-01-04 airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc ) df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)") # iterate over flights and pull the data from OpenSky Network flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time # fetch the data from OpenSky Network flights.append( opensky.history( start=start_time.strftime("%Y-%m-%d %H:%M:%S"), stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"), callsign=row["callsign"], return_flight=True, ) ) # The flights can be converted into a Traffic object Traffic.from_flights(flights)</code></pre> <p><strong>Additional files</strong></p> <p>Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:</p> <ul> <li><strong>validation_table.xlsx</strong>: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.</li> <li><strong>validation_sample.zip</strong>: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.</li> </ul>
On the Statistics and Predictability of Go-Arounds
This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.
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