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EP-4738236-A1 - TAXI TIME RESIDUAL CORRECTIONS

EP4738236A1EP 4738236 A1EP4738236 A1EP 4738236A1EP-4738236-A1

Abstract

Systems, devices, methods, and computer-readable media provide improved taxi-time estimates. A method includes receiving, by a graph neural network, GNN (112), features indicating current traffic and current weather conditions at an airport and an estimated taxi-time, the GNN (112) operating on a graph that indicates spatial relationships between elements of the airport, generating, by the GNN (112), graph data including weights that indicate an effect of the features on the taxi-time and a second estimated taxi-time, generating, by a transformer and based on the graph data and the second estimated taxi-time, taxi-time deviations, the taxi-time deviations indicating an amount of time to adjust the estimated taxi-time, and applying, by a taxi-time correction operator, the taxi-time deviations to the estimated taxi-time, resulting in an updated taxi-time.

Inventors

  • BALACHANDRAN, Vishnu
  • XAVIER, Jerrin

Assignees

  • ARINC Incorporated

Dates

Publication Date
20260506
Application Date
20251027

Claims (15)

  1. A method comprising: receiving, by a graph neural network, GNN (112), features indicating current traffic and current weather conditions at an airport and an estimated taxi-time, the GNN (112) operating on a graph that indicates spatial relationships between elements of the airport; generating, by the GNN (112), graph data including weights that indicate an effect of the features on the taxi-time and a second estimated taxi-time; generating, by a transformer and based on the graph data and the second estimated taxi-time, taxi-time deviations, the taxi-time deviations indicating an amount of time to adjust the estimated taxi-time; and applying, by a taxi-time correction operator, the taxi-time deviations to the estimated taxi-time, resulting in an updated taxi-time.
  2. The method of claim 1, further comprising adjusting airport operations based on the updated taxi-time.
  3. The method of claim 1 or 2, wherein the features further indicate airplane make and model of an airplane associated with the taxi-time.
  4. The method of any preceding claim, wherein the estimated taxi-time is based on historical averages of taxi-times.
  5. The method of any preceding claim, wherein the current weather conditions include wind speed and direction, precipitation, and temperature; and/or wherein the current traffic includes a number of vehicles present on taxiways that can be navigated by an airplane.
  6. The method of any preceding claim, further comprising generating the graph based on airport layout data, wherein the graph includes gates and taxiway interconnections as nodes and edges between nodes that are directly physically traversable, and optionally wherein the graph further include bottlenecks as nodes.
  7. The method of any preceding claim, further comprising training the GNN (112), transformer, and taxi-time correction operator based on actual taxi-times and corresponding taxi-time estimates, corresponding weather data, and corresponding traffic data.
  8. A system comprising: a graph neural network, GNN (112), configured to: operate on a graph that indicates spatial relationships between elements of an airport; receive features indicating current traffic and current weather conditions at the airport and an estimated taxi-time; and generate graph data including weights that indicate an effect of the features on the taxi-time and a second estimated taxi-time; a neural network transformer configured to generate, based on the graph data and the second estimated taxi-time, taxi-time deviations, the taxi-time deviations indicating an amount of time to adjust the estimated taxi-time; and processing circuitry configured to apply the taxi-time deviations to the estimated taxi-time, resulting in an updated taxi-time.
  9. The system of claim 8, wherein the processing circuitry is further configured to adjust a schedule of airport operations based on the updated taxi-time.
  10. The system of claim 8 or 9, wherein the features further indicate airplane make and model of an airplane associated with the taxi-time.
  11. The system of any of claims 8-10, wherein the estimated taxi-time is based on historical averages of taxi-times; and/or wherein the current weather conditions include wind speed and direction, precipitation, and temperature; and/or wherein the current traffic includes a number of vehicles present on taxiways that can be navigated by an airplane.
  12. The system of any of claims 8-11, wherein the processing circuitry is further configured to generate the graph based on airport layout data, wherein the graph includes gates and taxiway interconnections as nodes and edges between nodes that are directly physically traversable, and optionally wherein the graph further include bottlenecks as nodes.
  13. The system of any of claims 8-12, wherein the processing circuitry is further configured to train the GNN (112), transformer, and application of the taxi-time deviations based on actual taxi-times and corresponding taxi-time estimates, corresponding weather data, and corresponding traffic data.
  14. A non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to perform operations for aircraft taxi-time estimation, the operations comprising: receiving, by a graph neural network, GNN (112), features indicating current traffic and current weather conditions at an airport and an estimated taxi-time, the GNN (112) operating on a graph that indicates spatial relationships between elements of the airport; generating, by the GNN (112), graph data including weights that indicate an effect of the features on the taxi-time and a second estimated taxi-time; generating, by a transformer and based on the graph data and the second estimated taxi-time, taxi-time deviations, the taxi-time deviations indicating an amount of time to adjust the estimated taxi-time; and applying, by a taxi-time correction operator, the taxi-time deviations to the estimated taxi-time, resulting in an updated taxi-time.
  15. The non-transitory machine-readable medium of claim 14, wherein the operations further comprise adjusting airport operations based on the updated taxi-time.

Description

CLAIM OF PRIORITY This patent application claims the benefit of priority to India Application Serial No. 202411084085, filed November 4, 2024. TECHNICAL FIELD Embodiments regard increasing accuracy of airplane taxi times. BACKGROUND Current taxi times are predicted primarily on historical data. The historical data includes factors like typical taxi times for specific routes, average delays during different times of day, and seasonal trends. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 illustrates, by way of example, a diagram of an embodiment of a system for improved taxi time determination.FIG. 2 illustrates, by way of example, a diagram of an embodiment of a technique for training the GNN, the transformer, and the taxi-time correction operator.FIG. 3 illustrates, by way of example, a diagram of an embodiment of a technique for generating the GNN.FIG. 4 illustrates, by way of example, a diagram of an embodiment of a method for improved taxi time prediction.FIG. 5 is a block diagram of an example of an environment including a system for neural network (NN) training.FIG. 6 illustrates, by way of example, a block diagram of an embodiment of a machine in the example form of a computer system within which instructions, for causing the machine to perform any one or more of the methods or techniques discussed herein, may be executed. DETAILED DESCRIPTION The following description and the drawings sufficiently illustrate teachings to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. Portions and features of some examples may be included in, or substituted for, those of other examples. Teachings set forth in the claims encompass all available equivalents of those claims. Embodiments may be implemented in one or a combination of hardware, firmware and software. Embodiments may also be implemented as instructions stored on a computer-readable storage device, which may be read and executed by at least one processor to perform the operations described herein. A computer-readable storage device may include any non-transitory mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a computer-readable storage device may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, and other storage devices and media. Some embodiments may include one or more processors and may be configured with instructions stored on a computer-readable storage device. There are drawbacks to determining taxi times based on historical data. The drawbacks include an inability to adapt, a lack of specificity, a failure to account for complex interactions, and adverse impact on airport operations. Airport conditions are constantly changing. A sudden surge in traffic, a runway closure due to maintenance, or unexpected weather events can affect taxi times. Historical averages based on historical data do not change fast enough to reflect these real-time shifts. Historical data often treats taxi routes broadly and lack specificity. A flight departing from a far-off gate during peak traffic will take significantly longer to taxi than one from a closer gate during off-peak hours. Such a distinction that gets lost in broad averages provided by historical data. Taxiing is a complex process influenced by a multitude of factors and historical data fails to account for complex interactions. Taxiing is influenced by aircraft type and size. Larger aircraft may take longer to maneuver on taxiways and the historical data fails to account for this. Taxiing is influenced by route-specific adjustments. Historical data failes to factor in a starting gate location. If a large aircraft starts at a distant gate, the large aircraft effect on taxiing times should be amplified on the predicted route. The historical data fails to account for congestion. Bottlenecks at taxiway intersections or near a busy runway cause delays that affect taxi times. The historical data fails to account for weather. Rain, wind, or low visibility force pilots to taxi more cautiously, increasing taxi time. The historical data also fails to account for unexpected events. Mechanical issues, gate changes, or runway maintenance can all affect taxi times. The inaccuracy in taxi times adversely affects airport operations. The innacuracy has cascading effects on airport operations. The innacuracy leads to sequencing inefficiencies, suboptimal gate assignments, and disrupted ground handling, among other adverse affects on airport operations. Regarding sequencing inefficiencies, takeoff and landing sequences are often planned around expected taxi times. Wrong estimates of taxi times leads to aircraft waiting unnecessarily on the tarmac or rushing to runways, causing further delays down the line. Regarding suboptimal gate assignments, if taxi times are consistently underestimated, airlines can choose