Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

For the purposes of this paper, the National Airspace System (NAS) encompasses the operations of all aircraft which are subject to air traffic control procedures. The NAS is a highly complex dynamic system that is sensitive to aeronautical decision-making and risk management skills. In order to ensure a healthy system with safe flights a systematic approach to anomaly detection is very important when evaluating a given set of circumstances and for determination of the best possible course of action. Given the fact that the NAS is a vast and loosely integrated network of systems, it requires improved safety assurance capabilities to maintain an extremely low accident rate under increasingly dense operating conditions. Data mining based tools and techniques are required to support and aid operators’ (such as pilots, management, or policy makers) overall decision-making capacity. Within the NAS, the ability to analyze fleetwide aircraft data autonomously is still considered a significantly challenging task. For our purposes a fleet is defined as a group of aircraft sharing generally compatible parameter lists. Here, in this effort, we aim at developing a system level analysis scheme. In this paper we address the capability for detection of fleetwide anomalies as they occur, which itself is an important initiative toward the safety of the real-world flight operations. The flight data recorders archive millions of data points with valuable information on flights everyday. The operational parameters consist of both continuous and discrete (binary & categorical) data from several critical subsystems and numerous complex procedures. In this paper, we discuss a system level anomaly detection approach based on the theory of kernel learning to detect potential safety anomalies in a very large data base of commercial aircraft. We also demonstrate that the proposed approach uncovers some operationally significant events due to environmental, mechanical, and human factors issues in high dimensional, multivariate Flight Operations Quality Assurance (FOQA) data. We present the results of our detection algorithms on real FOQA data from a regional carrier.

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2].

The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows:

• the top level key is the id of the airport, the value is list a of all regulations available for this airport.

• each item of the list is a dictionary, with keys:

  -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.

  -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.

  -- 'flight_ids': list of flight ids (in the same order than etas)

  -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3].

  -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline.

[1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600

[2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021.

Pre-print available here: https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms

[3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a

Passengers enplaned and deplaned at Canadian airports, annual.

The National Security UAS Flight Restrictions in this dataset are currently pending and will become effective on May 05, 2023. The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282 . Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week. WHAT UAS FLIGHT RESTRICTIONS HAVE BEEN PUT INTO PLACE? At the request of and pursuant to agreements with the Department of Defense and U.S. Federal security and intelligence agencies (“sponsoring Federal agencies”), the Federal Aviation Administration (FAA) has implemented Special Security Instructions for Unmanned Aircraft System (UAS), issued as temporary flight restrictions (TFR) over select national security sensitive facilities located throughout the U.S. These TFRs are established within the lateral boundaries of these facilities and extend from surface to 400 feet Above Ground Level (AGL). These TFRs apply to all UAS operations specifically including: · Public aircraft operations conducted in accordance with a Certificate of Authorization or Waiver (COA). · Civil aircraft operations (other than model aircraft), including those conducted in accordance with a COA and those conducted in accordance with the FAA’s small UAS Rule, 14 CFR Part 107. · Model Aircraft operations conducted in accordance with 14 CFR Part 101, Subpart E. UAS operators must comply with these flight restrictions in addition to all other applicable Federal Aviation Regulations, including but not limited to, requirements to secure an FAA airspace authorization and/or waiver prior to flying in the airspace where a TFR is in effect. The information on this website complements Notice to Airmen (NOTAM) NOTAM FDC 7/7282, which generally notifies the public about these temporary flight restrictions (TFR). This website provides UAS operators with more detailed information about these TFRs, including: · An explanation of what is restricted · A table listing the selected facilities over which a TFR has been established · An interactive map providing visual depictions and information about specific TFRs and geospatial (GIS) data that can be downloaded · An explanation of which UAS operations may be able to access the airspace within a TFR, including instructions for submitting a request · Reminders on other requirements for UAS operations WHAT HAPPENS IF I VIOLATE A TEMPORARY FLIGHT RESTRICTION (TFR)? The FAA classifies the airspace encompassed by these temporary flight restrictions (TFRs) as “national defense airspace” in accordance with Title 49 of the United States Code (USC) § 40103(b)(3). Violations of these TFRs may prompt the following enforcement actions: A. The U.S. Government may pursue criminal charges, including charges under Title 49 U.S.C § 46307. B. The FAA may take administrative action, including imposing civil penalties and the revoking FAA certificates and authorizations to operate UAS under Title 49 U.S.C. §§ 44709 and 46301. WHAT ARE THE BASIC FLIGHT RESTRICTIONS? The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282. Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week. See the full text of NOTAM FDC 7/7282 here. ARE THERE EXCEPTIONS FOR UAS OPERATIONS TO ACCESS A TFR? The FAA has authorized UAS operations within the TFRs if those flights are in compliance with the applicable requirements listed below: 1) The UAS flight operation has been pre-approved by the designated facility contact based on criteria established by the sponsoring federal agency in coordination with the FAA. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and secure permission to operate within the airspace prior to entry. Pre-approval from the facility or sponsoring agency does not substitute for compliance with FAA requirements. Depending on the nature of the proposed operation and Class of airspace, waiver or authorization may be needed from the FAA before flight. For more information visit our website at www.faa.gov/uas 2). The UAS flight operation is conducted in direct support of an active national defense, homeland security, law enforcement, firefighting, search and rescue, or disaster response mission, and prior notification has been provided to the designated facility contact. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and provide notification prior to entering the airspace. These operators must make every effort to coordinate with the designated facility to deconflict the UAS flight operation with any safety or security concerns stated by the facility and/or sponsoring Federal agency. 3). The UAS flight operation is conducted in direct support of a significant and urgent governmental interest and is approved by the FAA’s System Operations Support Center (SOSC) in advance of entering the TFR. Note: UAS operators, that meet the criteria for thisprovision , may also qualify for access under provision 2 outlined above and are encouraged to coordinate directly with the facility’s designated point of contact identified in the table or interactive map, by providing notification prior to entering the airspace and taking into consideration any safety or security concerns stated by the facility and/or sponsoring Federal agency. For urgent and time sensitive requests, contact the FAA’s SOSC at (202) 267-8276 for expedited assistance. The FAA’s SOSC will coordinate with the facility and/or sponsoring Federal agency as appropriate. ARE THERE OTHER REQUIREMENTS TO OPERATE IN A TFR IN ADDITION TO THE EXCEPTIONS? Separate and distinct from any of the conditions cited above used to gain access to a TFR defined by NOTAM FDC 7/7282 and described in this website, UAS operators must comply with all applicable Federal Aviation Regulations. For example: For Model Aircraft: · Comply with 14 CFR Part 101, Subpart E NOTE: These provisions require model aircraft operators to notify any airport operator and air traffic control tower within 5 miles of the intended area of flight. For All Other UAS Operators: · Comply with a Public Aircraft Certificate of Authorization or Waiver (COA), or · Comply with 14 CFR Part107, Small Unmanned Aircraft Systems, or · Comply with Section 333 Exemption and a Certificate of Authorization or Waiver (COA) NOTE: Public and civil UAS operators flying under the provisions of a COA or 14 CFR Part 107 may need to secure further airspace authorizations or waivers in order to conduct the proposed flight operation in controlled airspace, which may overlap with one of the TFRs defined by NOTAM FDC 7/7282 and this website. In those cases, these operators should follow the pre-existing procedures outlined below. A. Non-emergency requests for UAS airspace authorizations and waivers must be submitted using the regular process as follows: · 14 CFR Part 107 requests for airspace authorizations and waivers must be submitted to the FAA at https://www.faa.gov/uas/request_waiver/ · Section 333 Exemption holders may request a site specific COA at https://oeaaa.faa.gov/oeaaa/external/uas/portal.jsp · Public aircraft operators without an existing authorization to operate must secure a public COA athttps://ioeaaa.faa.gov/oeaaa/Welcome.jsp B. Emergency requests for UAS authorizations/waivers for missions that directly support significant and urgent governmental interests (e.g., active national defense, homeland security, law enforcement, and emergency operations missions), which cannot be supported by the FAA’s routine authorization/waiver processes should be referred to the SOSC at (202) 267-8276 ADDITIONAL QUESTIONS? If you have any general questions regarding UAS operations, please refer to the following FAA webpage: https://www.faa.gov/uas/ , or contact the FAA by email at uashelp@faa.gov or by phone at (844) FLY-MY-UA. If you have any additional questions regarding the TFRs defined by NOTAM FDC 7/7282 and this website, please contact the FAA SOSC at (202) 267-8276. Disclaimers The restrictions depicted on this site reflect temporary flight restrictions issued for national security reasons at select U.S. Federal facilities. There may be additional temporary flight restrictions that prohibit UAS and manned flight in effect in your area. See https://tfr.faa.gov/tfr2/list.html for additional information on flight restrictions that may be in effect in your area before operating your UAS.

Data_Dictionary_Flight_on_time_HIX

Airline: Operating airline

Flight_Number: Flight number

Plane_ID: Plane identifier

FlightDate: Date of flight

Origin_Airport: Origin airport code

Destination_Airport: Destination airport code

Flight_Distance: Distance (miles) between origin and destination

Scheduled_Departure_Time: Scheduled departure time (local time: hhmm)

Actual_Departure_Time: Actual departure time (local time: hhmm)

Departure_Delay_Minutes: Difference in minutes between scheduled and actual departure time. Early departures set to 0.

Departure_Taxi: Taxi Out Time, in Minutes

Departure_WheelsOff: Wheels Off Time (local time: hhmm)

Scheduled_Arrival_Time: Scheduled arrival time (local time: hhmm)

Actual_Arrival_Time: Actual arrival time (local time: hhmm)

Arrival_Delay_Minutes: Difference in minutes between scheduled and actual arrival time. Early arrivals set to 0.

Arrival_Taxi: Taxi In Time, in Minutes

Arrival_WheelsOn: Wheels On Time (local time: hhmm)

Delay_Reason: Major reason for delay

Data_Dictionary_Airport_Weather

airport: Airport name

time: The datetime at which this data point begins.

summary: A human-readable text summary of this data point.

precipIntensity: The intensity (in inches of liquid water per hour) of precipitation occurring at the given time.

precipProbability: The probability of precipitation occurring, between 0 and 1, inclusive.

temperature: The air temperature in degrees Fahrenheit.

apparentTemperature: The apparent (or “feels like”) temperature in degrees Fahrenheit.

dewPoint: The dew point in degrees Fahrenheit.

humidity: The relative humidity, between 0 and 1, inclusive.

pressure: The sea-level air pressure in millibars.

windSpeed: The wind speed in miles per hour.

windGust: The wind gust speed in miles per hour.

windBearing: The direction that the wind is coming from in degrees, with true north at 0° and progressing clockwise. (If windSpeed is zero, then this value will not be defined.)

cloudCover: The percentage of sky occluded by clouds, between 0 and 1, inclusive.

uvIndex: The UV index. visibility: The average visibility in miles, capped at 10 miles.

ozone: The columnar density of total atmospheric ozone at the given time in Dobson units.

precipType: The type of precipitation occurring at the given time. If defined, this property will have one of the following values: "rain", "snow", or "sleet" (which refers to each of freezing rain, ice pellets, and “wintery mix”)

precipAccumulation: The amount of snowfall accumulation expected to occur (over the hour or day, respectively), in inches. (If no snowfall is expected, this property will not be defined.)

Data set for the theme Cadastral parcels (CP) harmonised according to the INSPIRE Directive and data specification for ELF version 1.0. The data contain the boundaries of cadastral territories, parcels and parcel numbers. The data set is provided as open data (CC-BY 4.0 license). The data is based on ISKN (Cadastre Information System). Data are only available in those cadastral areas where the cadastral map is in digital form — (as at 10. 01. 2022 is 97.82 % of the territory of the Czech Republic, i.e. 77 150.84 km²). The data is generated daily (if any change occurs within the cadastral area). Data in GML 3.2.1 are valid against the XML schema for INSPIRE theme Cadastral parcels version 4.0 and against the schematic for spatial data ELF version 1.0. Data is compressed for download (ZIP). More cadastral Act 256/2013 Coll., Decree on the Land Registry No 357/2013 Coll., Decree on the Provision of Data No. 358/2013 Coll., as amended, and INSPIRE Data Specification on cadastral Parcels v 3.0.1.

The volume of air-freight transport in the United Arab Emirates was forecast to decrease between 2024 and 2029 by in total 0.02 billion ton-kilometers. This overall decrease does not happen continuously, notably not in 2026 and 2027. The volume of air-freight transport is estimated to amount to 14 billion ton-kilometers in 2029. As defined by Worldbank, air freight refers to the summated volume of freight, express and diplomatic bags carried across the various flight stages (from takeoff to the next landing). The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the volume of air-freight transport in countries like Oman and Israel.

This dataset contains maternal reproductive output data, embryonic development data and offspring performance data for the Speckled Wood butterfly, Pararge aegeria. The data were collected from a laboratory experiment testing the hypothesis that repeat periods of intensive flight during female oviposition affects egg provisioning and reduces offspring performance when larval development occurs on drought stressed host plants. The experiment involved stimulating female butterflies to fly for 5 minutes for 3 periods during oviposition; removing eggs from 5 different days during oviposition to be monitored for hatching; and removing a larva on day of hatching to be reared on a drought stressed host plant. For each larva, development time from hatching to pupation, pupal mass and survival to eclose as an adult was recorded. On eclosion, each offspring adult was sexed and the thorax weighed. The overall aim of this experimental work was to explore one of the potential mechanisms for the impact of drought and habitat fragmentation on biodiversity.