Apparatus and method for terminal program prediction of flight plan and readable medium

Abstract

The invention relates to equipment and a method for terminal program prediction of a flight plan and a readable medium. The method comprises the following steps: obtaining input data corresponding to an upcoming flight of an aircraft at a processor; obtaining, at the processor, prediction data corresponding to the impending flight; and processing, at a processor, the input data and the predicted data to obtain a terminal program prediction associated with the impending flight, the terminal program prediction being based at least in part on a flight plan of historical flights having similar conditions to the input data and the predicted data.

Description

Technical Field

[0001] The present disclosure generally relates to apparatus and methods for terminal procedure prediction for flight planning. Background Art

[0002] As air traffic continues to increase, efficient aircraft management becomes correspondingly more important. One aspect of aircraft management includes generating an initial flight plan for each aircraft, which includes one or more terminal procedures associated with each departure and arrival airport. Typically, terminal procedures are created several hours before an upcoming flight by individuals based on their experience, judgment, and currently available information about the four hours prior to the flight.

[0003] With the increased difficulty of modern air traffic, and the ubiquity of complicating factors such as changing weather, it is often necessary to modify flight plans to select new terminal procedures while an aircraft is in flight. These additional tasks can burden the crew, distracting their attention from other critical flight tasks, such as maintaining the safety of passengers and the efficiency of the aircraft's own operations. Summary of the invention

[0004] In a specific implementation, a device includes one or more processors configured to obtain input data corresponding to an upcoming flight of an aircraft. The processor is further configured to obtain prediction data corresponding to the upcoming flight. The processor is further configured to process the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data.

[0005] In another specific implementation, a method includes obtaining, at a processor, input data corresponding to an upcoming flight of an aircraft. The method also includes obtaining, at the processor, prediction data corresponding to the upcoming flight. The method also includes processing, at the processor, the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data.

[0006] In another specific implementation, a non-transitory computer-readable medium includes instructions that, when executed by one or more processors, cause the processors to obtain input data corresponding to an upcoming flight of an aircraft. The instructions also cause the processors to obtain prediction data corresponding to the upcoming flight. The instructions also cause the processors to process the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on a flight plan of a historical flight having conditions similar to the input data and the prediction data.

[0007] In another specific implementation, an apparatus includes means for obtaining, at a processor, input data corresponding to an upcoming flight of an aircraft. The apparatus also includes means for obtaining, at the processor, prediction data corresponding to the upcoming flight. The apparatus also includes means for processing, at the processor, the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data.

[0008] The features, functions, and advantages described herein can be achieved independently in various implementations or may be combined in yet other implementations, further details of which can be found with reference to the following description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 An exemplary system for terminal procedure prediction for flight planning is shown in accordance with some embodiments of the present disclosure.

[0010] Figure 2 An exemplary graph showing a historical planned flight path of an aircraft traveling about a plurality of waypoints with respect to a first historical actual flight path and a second historical actual flight path according to some implementations of the present disclosure;

[0011] Figure 3 An exemplary architecture for terminal procedure prediction for flight planning is shown in accordance with some implementations of the present disclosure.

[0012] Figure 4 is a flow chart of an exemplary method for terminal procedure prediction for flight planning according to some embodiments of the present disclosure.

[0013] Figure 5 is a block diagram of a computing environment including a computing device configured to support aspects of computer-implemented methods and computer-executable program instructions (or code) according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0014] Accurate, automated, data-driven predictions of terminal procedures are not typically available. The disclosed systems and methods leverage existing databases, contextual information, and condition predictions to generate accurate predictions of the most likely terminal procedure for a particular flight of an aircraft, and in some implementations, also generate predictions of the likelihood and location of direct approaches (e.g., deviations from a planned flight path approved by an air traffic control system) that may modify the flight plan.

[0015] Terminal procedures help ensure the safety of aircraft operations maintained by a combination of airlines, airports, and regulatory agencies such as the Federal Aviation Administration. For example, terminal procedures include instructions for aircraft arriving at a particular airport from a flight, and managing the flow of arriving aircraft into busy terminal areas. Terminal procedures may also include a series of predetermined waypoints, altitude and speed restrictions, and communication protocols between pilots and air traffic controllers.

[0016] A technical advantage of the present disclosure is the ability to automatically and efficiently use historical data, contextual information, and conditional predictions to generate accurate predictions of the appropriate terminal procedures an aircraft will require at its arrival and departure airports. The tail-specific model can be used in a wide range of data-driven decision-making processes, such as improved aircraft maintenance and fuel consumption optimization.

[0017] The accompanying drawings and the following description illustrate specific exemplary embodiments. It should be understood that those skilled in the art will be able to design various arrangements that, although not explicitly described or shown herein, embody the principles described herein and are included within the scope of the claims appended to this description. In addition, any embodiments described herein are intended to aid in understanding the principles of the present disclosure and are to be interpreted as not limiting. Therefore, the present disclosure is not limited to the specific embodiments or embodiments described below, but is defined by the claims and their equivalents.

[0018] Specific implementations are described herein with reference to the accompanying drawings. In the description, common features are represented by common reference numerals throughout the drawings. As used herein, different terms are used only for the purpose of describing specific implementations and are not intended to be limiting. For example, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Further, some features described herein are singular in some implementations and plural in other implementations. For illustration, Figure 1 Describes a method comprising one or more processors ( Figure 1106), which means that in some implementations the system 100 includes a single processor 106, and in other implementations the system 100 includes multiple processors 106. For ease of reference herein, such features are generally introduced as "one or more" features and are then represented in the singular or optionally the plural, unless aspects related to multiple features are being described.

[0019] The terms "include," "comprises," and "cover" are used interchangeably with "have," "contains," or "included." Additionally, the term "wherein" is used interchangeably with the term "in which." As used herein, "exemplary" indicates an embodiment, implementation, and / or aspect, and should not be interpreted as limiting or indicating a preference or preferred implementation. As used herein, ordinal terms (e.g., "first," "second," "third," etc.) used to modify an element (such as a structure, component, operation, etc.) do not themselves indicate any priority or order of the element relative to another element, but merely distinguish the element from another element with the same name (but for use of the ordinal term). As used herein, the term "set" refers to a grouping of one or more elements, and the term "plurality" refers to a plurality of elements.

[0020] As used herein, "generate", "calculate", "use", "select", "access" and "determine" are interchangeable unless the context indicates otherwise. For example, "generate", "calculate" or "determine" a parameter (or signal) may refer to actively generating, calculating or determining a parameter (or signal) or may refer to using, selecting or accessing a parameter (or signal) that has been generated, such as by another component or device. As used herein, "coupling" may include "communicative coupling", "electrical coupling" or "physical coupling", and may also (or alternatively) include any combination thereof. Two devices (or components) may be directly or indirectly coupled (e.g., communicatively coupled, electrically coupled or physically coupled) via one or more other devices, components, wires, buses, networks (e.g., wired networks, wireless networks, or combinations thereof), etc. Two electrically coupled devices (or components) may be included in the same device or in different devices, and may be connected via electronic devices, one or more connectors, or inductive coupling, as illustrative, non-limiting embodiments. In some implementations, two devices (or components) that are communicatively coupled (e.g., in electrical communication) may directly or indirectly send and receive electrical signals (digital or analog), for example, via one or more wires, buses, networks, etc. As used herein, "directly coupled" is used to describe two devices that are coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) without intervening components.

[0021] As used herein, the term "machine learning" should be understood to have any of its usual and customary meanings within the fields of computer science and data science, including, for example, a process or technique by which one or more computers can learn to perform an operation or function without being explicitly programmed to perform an operation or function. As a typical embodiment, machine learning can be used to enable one or more computers to analyze data to identify patterns in the data and generate results based on the analysis. For some types of machine learning, the generated results include data indicating the underlying structure or pattern of the data itself. Such techniques include, for example, so-called "clustering" techniques, which identify clusters (e.g., groupings of data elements of data).

[0022] For some types of machine learning, the generated results include a data model (also referred to as a "machine learning model" or simply "model"). Typically, a model is generated using a first data set to facilitate analysis of a second data set. For example, a first portion of a large set of data can be used to generate a model, which can be used to analyze the remainder of the large set of data. As another example, a set of historical data can be used to generate a model that can be used to analyze future data.

[0023] Because the model can be used to evaluate a set of data different from the data used to generate the model, the model can be regarded as a type of software (e.g., instructions, parameters, or both) that is automatically generated by a computer during the machine learning process. As such, the model can be portable (e.g., it can be generated at a first computer and then moved to a second computer for further training, for use, or both). In addition, the model can be used in conjunction with one or more other models to perform the desired analysis. For illustration, the first data can be provided as input to a first model to generate a first model output data, and the first model output data (alone, together with the first data, or together with other data) can be provided as input to a second model to generate a second model output data indicating the result of the desired analysis. Depending on the analysis and data involved, different combinations of models can be used to generate such results. In some embodiments, multiple models can provide model outputs that are input to a single model. In some embodiments, a single model provides model outputs as input to multiple models.

[0024] Embodiments of machine learning models include, but are not limited to, perceptrons, neural networks, support vector machines, regression models, decision trees, Bayesian models, Boltzmann machines, adaptive neuro-fuzzy inference systems, and combinations, collections, and variants of these and other types of models. Variants of neural networks include, for example, but are not limited to, prototype networks, autoencoders, transformers, self-attention networks, convolutional neural networks, deep neural networks, deep belief networks, etc. Variants of decision trees include, for example, but are not limited to, random forests, boosted decision trees, etc.

[0025] Since machine learning models are generated by (multiple) computers based on input data, machine learning models can be discussed in terms of at least two different time windows - a creation / training phase and a runtime phase. During the creation / training phase, a model is created, trained, adapted, validated, or otherwise configured by a computer based on input data (which is generally referred to as "training data" in the creation / training phase). Note that a trained model corresponds to software that has been generated and / or refined during the creation / training phase to perform a specific operation (such as classification, prediction, encoding, or other data analysis or data synthesis operations). During the runtime phase (or "inference" phase), the model is used to analyze the input data to generate a model output. The content of the model output depends on the type of model. For example, a model can be trained to perform a classification task or a regression task, as a non-limiting example. In some implementations, the model may be updated continuously, periodically, or occasionally, in which case the training time and runtime may be intertwined, or one version of the model may be used for inference while a copy is updated, after which the updated copy may be deployed for inference.

[0026] In some implementations, machine learning techniques are used to train (or retrain) a previously generated model. In this context, "training" refers to adapting a model or parameters of a model to a specific data set. Unless otherwise clear from the specific context, the term "training" as used herein includes "retraining" or refining a model for a specific data set. For example, training can include so-called "transfer learning". In transfer learning, a base model can be trained using a general or typical data set, and then the base model can be refined (e.g., retrained or further trained) using a more specific data set.

[0027] The data set used during training is called the "training data set" or simply "training data". A data set can be labeled or unlabeled. "Labeled data" refers to data that has been assigned a class label indicating a group or category associated with the data, and "unlabeled data" refers to unlabeled data. Typically, a "supervised machine learning process" uses labeled data to train a machine learning model, while an "unsupervised machine learning process" uses unlabeled data to train a machine learning model; however, it should be understood that the label associated with the data is itself just another data element that can be used in any appropriate machine learning process. For illustration, many clustering operations can operate using unlabeled data; however, such clustering operations can use labeled data by ignoring the labels assigned to the data or by treating the labels the same as other data elements.

[0028] Training a model based on a training data set generally includes changing the parameters of the model based on the data input to the model, and its goal is to make the output of the model have specific characteristics. In order to distinguish from the model generation operation, model training can be referred to as optimization or optimization training in this article. In this context, "optimization" refers to improving a metric, and does not mean finding the ideal value of the metric (e.g., global maximum or global minimum). Embodiments of optimization trainers include but are not limited to back propagation trainers, derivative-free optimizers (DFO) and extreme learning machines (ELM). As an embodiment of a training model, during the supervised training of a neural network, an input data sample is associated with a label. When the input data sample is provided to the model, the model generates output data, which is compared with the label associated with the input data sample to generate an error value. Modify the parameters of the model in an attempt to reduce (e.g., optimize) the error value. As another embodiment of a training model, during the unsupervised training of an autoencoder, a data sample is provided to the autoencoder as input, and the autoencoder reduces the dimensionality of the data sample (which is a lossy operation) and attempts to reconstruct the data sample as output data. In this embodiment, the output data is compared to the input data samples to produce a reconstruction loss, and the parameters of the autoencoder are modified in an attempt to reduce (eg, optimize) the reconstruction loss.

[0029] Figure 1 An exemplary system 100 for terminal procedure prediction of a flight plan according to some embodiments of the present disclosure is shown. In some implementations, the system 100 includes a computing device 102 configured to communicate with one or more devices 104. The computing device 102 may include one or more processors 106 coupled to a memory 108, which includes instructions that, when executed by the processor 106, cause the processor 106 to perform certain functions such as those described in detail below.

[0030] The processor 106 may include one or more components configured to perform one or more of those functions, including a terminal procedure predictor 120, a historical flight selector 126, or some combination thereof. In some aspects, the terminal procedure predictor 120 may be configured to obtain a terminal procedure prediction 122 including data associated with a prediction for a terminal procedure, a prediction for a probability that the predicted terminal procedure will be effective for all characteristic flights, or both. In characteristic aspects, the terminal procedure predictor 120 may include a machine learning model 124 configured to provide the terminal procedure prediction 122 based at least on the training data 118. In some implementations, the historical flight selector 126 may be configured to identify a set of historical flights 128 based on one or more comparisons of the input data 110 and the predicted data with some condition of the historical flights, as described in more detail below.

[0031] The memory 108 may include data that enables the systems and methods described herein for terminal procedure prediction for flight plans. For example, the memory 108 may include prediction data 114, conditions 115, historical flight data 116, other data for implementing terminal procedure prediction for flight plans, or any combination thereof.

[0032] In some aspects, prediction data 114 may include runway prediction data 150, weather forecast data 152, one or more airport traffic models 154, etc. In the same or alternative aspects, conditions 115 may include data associated with current conditions for a particular flight. For example, conditions 115 may include data associated with contextual information for the flight, such as navigation data, current weather data, runway usage data, a Notification of Airborne Mission (“NOTAM” or “NOTAMs”), other information associated with the context for a particular upcoming flight, or some combination thereof.

[0033] In some implementations, the historical flight data 116 may include data values ​​associated with historical flights. For example, the historical flight data 116 may include the flight plan 112 including data associated with the flight plan of the historical flight, the historical conditions 130 including data associated with the conditions of the historical flight (e.g., weather, runway usage, etc.).

[0034] Computing device 102 is configured to receive input data 110 generated by device 104. Device 104 may be any suitable electronic device configured to transmit input data 110 to computing device 102. Device 104 may include one or more processors 132 coupled to memory 134 that includes instructions that, when executed by processor 132, cause processor 132 to perform certain functions such as those described in detail below.

[0035] In some aspects, the memory 134 may also include upcoming flight data 136 associated with a particular upcoming flight. For example, the upcoming flight data 136 may include data for a specific upcoming flight associated with date and time information 138, a departure airport 140, an arrival airport 142, an aircraft type 144, an aircraft configuration 146, weight and balance data 148, etc. In some implementations, the device 104 may be configured to provide the input data 110 based on one or more data values ​​of the upcoming flight data 136.

[0036] In operation, the processor 106 may be configured to obtain input data 110 corresponding to an upcoming flight of an aircraft. For example, the device 104 may send input data 110 corresponding to a particular upcoming flight indicating that the flight will occur today, February 7, within four hours, departing from Los Angeles International Airport and arriving at Chicago O'Hare with an estimated arrival time of 10:30 PM Central Time. In an illustrative embodiment, the aircraft for the flight will be a Boeing 737 with a specific configuration and with a specific weight and balance.

[0037] The processor 106 may also be configured to obtain forecast data 114 corresponding to the upcoming flight. Continuing with the illustrative embodiment above, the terminal procedure predictor 120 may be configured to obtain data including a prediction of a specific runway that the aircraft will use at Chicago O'Hare upon arrival, data associated with the weather forecast for Chicago at 10:30 PM Central Time on February 7, and data associated with a model of how busy aircraft traffic will be at O'Hare upon arrival.

[0038] The processor 106 may also be configured to process the input data 110 and the prediction data 114 to obtain a terminal procedure prediction 122 associated with the upcoming flight. Continuing with the illustrative embodiment above, the historical flight selector 126 may be configured to process the input data 110 describing an upcoming flight from Los Angeles to Chicago and data associated with a prediction of conditions in Chicago at the arrival time of the particular flight. In some implementations, the terminal procedure prediction 122 may be based at least in part on flight plans 112 of historical flights having historical conditions 130 similar to the input data 110 and the prediction data 114. For example, the processor 106 may be configured to analyze flight plans of historical flights from Los Angeles to Chicago near the time of the upcoming flight in early February under conditions similar to the predicted conditions in Chicago near the arrival time of the upcoming flight to obtain a terminal procedure prediction for the upcoming flight. For example, if the predicted data indicates that the weather in Chicago will be cold and rainy, at least one runway will be closed due to inclement weather, and aircraft traffic is expected to be relatively congested, and flight plans for historical flights with similar conditions indicate the use of a particular terminal procedure upon arrival, terminal procedure predictor 120 may be configured to obtain a forecast for the particular terminal procedure.

[0039] In some aspects, the terminal procedure predictor 120 is configured to process the input data 110 and the prediction data 114 using a machine learning model 124 trained on the training data 118. In a particular configuration, the training data 118 is based on the historical flight data 116.

[0040] In some aspects, the historical flight selector 126 may be configured to access the historical flight data 116 to identify a set of historical flights 128 based on one or more comparisons of the input data 110 and the prediction data 114 to the historical conditions 130. In certain aspects, the terminal procedure prediction 122 is obtained based on a statistical analysis of the set of historical flights 128. For example, the historical flight selector 126 may be configured to analyze a set of flights from Los Angeles to Chicago in early February over the past five years when weather conditions were similar to those predicted for the upcoming flight.

[0041] In some aspects, the processor 106 may also be configured to obtain aviation notification information (e.g., NOTAMs). The terminal procedure prediction 122 may then be based on the aviation notification information. NOTAMs may include, for example, potential hazards along a flight path or at locations that may affect a flight (e.g., construction near an airport, special events occurring near an airport and / or along a flight path, closed runways, airport construction, etc.).

[0042] In some aspects, the terminal program prediction 122 may include multiple waypoints. The processor 106 may be configured to determine the likelihood of approval of a request for a direct route that bypasses one or more waypoints, as described below with reference to Figure 2 In certain aspects, the processor 106 may be configured to analyze flight position data (eg, the flight plan 112 ) of historical flights and detect whether one or more waypoints were bypassed during the historical flights.

[0043] In some aspects, the processor 106 may be configured to determine a fuel safety margin based on a flight plan for an upcoming flight that includes a terminal procedure prediction 122. Generally, the fuel safety margin may include an amount of fuel carried by the aircraft to ensure safe operation of the flight while still optimizing the weight of the aircraft. Generating an accurate terminal procedure prediction (including the possibility of approving a request for a direct route that bypasses one or more waypoints) may allow the flight planner to potentially reduce (or increase) the amount of fuel required for a particular flight plan while still ensuring safe operation of the upcoming flight.

[0044] In some implementations, the device 104 may be associated with, integrated into, or otherwise included in a flight dispatch system. In such implementations, the processor 106 may also be configured to submit the terminal procedure prediction 122 to an air traffic control approval system. The air traffic control approval system may be configured to approve the terminal procedure prediction 122, reject the terminal procedure prediction 122, recommend a new terminal procedure, or some combination thereof. Obtaining accurate data-driven terminal procedure predictions 122 may improve the efficiency of the air traffic control approval system.

[0045] In the same or alternative implementations, the device 104 may be associated with, integrated into, or otherwise included in an air traffic control approval system. In such an implementation, the processor 106 may be configured to compare the terminal procedure prediction 122 to a flight plan for an upcoming flight that has been submitted to the air traffic control approval system. As described above, obtaining accurate data-driven terminal procedure predictions 122 may improve the efficiency of the air traffic control approval system.

[0046] The system 100 may also include Figure 1 Components not shown in the figure. For example, computing device 102 may also include a receiver configured to receive input data 110 from device 104. The receiver may be configured to receive data via a radio frequency link, for example. As additional embodiments, system 100 may also include one or more input / output interfaces, one or more network interfaces, etc. In addition, although Figure 1 The memory 108 of the system 100 is shown as storing certain data, but more, less, and / or different data may be present within the memory 108 without departing from the scope of the present disclosure.

[0047] In addition, although Figure 1 Some operations are shown occurring within computing device 102, but these operations may be performed by other components of system 100 without departing from the scope of this disclosure. For example, one or more components external to computing device 102 may be configured to host or otherwise incorporate some or all of machine learning model 124, training data 118, terminal procedure predictor 120, historical flight selector 126, or some combination thereof. Such components may be located remotely from computing device 102 and accessed via a modem of computing device 102. As a particular embodiment, historical flight data 116, prediction data 114, or some combination thereof may be stored in one or more databases external to computing device 102.

[0048] In addition, despite Figure 1 Computing device 102 and device 104 are shown as separate, but other configurations are possible without departing from the scope of the present disclosure. For example, computing device 102 and device 104 may be integrated into an air traffic control approval system. As an additional embodiment, one or more components of computing device 102 may be distributed across multiple computing devices (e.g., a group of servers).

[0049] Figure 2 An exemplary graph 200 depicts a comparison of a historical planned flight path of an aircraft 202 traveling near a plurality of waypoints 214, 216, 218 relative to a first historical actual flight path and a second historical actual flight path according to some implementations of the present disclosure. Figure 1 The comparison may be performed by one or more components of the processor 106 of the system. Waypoints 214, 216, 218 may be geographic locations associated with one or more landmarks of a particular flight. For example, a given waypoint may be associated with a departure airport, an arrival airport, a particular altitude at a particular location, etc.

[0050] In exemplary diagram 200, aircraft 202 is shown traveling along a route from first waypoint 214 to its current location. The planned flight path of aircraft 202 includes first segment 204 between first waypoint 214 and second waypoint 216, second segment 206 between second waypoint 216 and third waypoint 218, and third segment 208 from third waypoint 218 to the current location of aircraft 202. In some implementations, the planned flight path may generally correspond to a flight plan filed by aircraft 202 prior to aircraft 202 conducting a particular flight. In certain configurations, the flight plan may be stored as an operational flight plan ("OFP") and stored in a database for analysis, as described below with reference to FIG. Figure 3 Described in more detail.

[0051] Although the planned flight path is included in the flight plan, the exact position of the aircraft on a given flight can deviate from the planned flight plan. In most commercial flights, the aircraft periodically reports its position during the course of its flight. By analyzing the aggregate of these position reports, the system (e.g., Figure 1 The system 100 may be configured to compare the actual flight path with the planned flight path. For example, Figure 1 The processor 106 may be configured to generate polygons 220, 222, 224, respectively, around the geographic locations associated with each of the waypoints 214, 216, 218. Generating the polygons 220, 222, 224 may allow the system to capture the actual location of the aircraft 202 within a threshold distance of the waypoints 214, 216, 218 or within a defined area around the waypoints 214, 216, 218, respectively.

[0052] For example, data from aircraft 202 indicates that in a first historical flight, aircraft 202 traveled along first segment 210 from first polygon 220 around first waypoint 214, through second polygon 222 around second waypoint 216, and to third polygon 224 around third waypoint 218. From there, aircraft 202 traveled along segment 212 from third polygon 224 around third waypoint 218 to its current location. By analyzing the actual historical flight path of aircraft 202 relative to polygons 220, 222, 224, the system can identify an actual flight path with a particular waypoint.

[0053] As another example, data from aircraft 202 indicates that in a second historical flight, aircraft 202 traveled along first segment 204 from first waypoint 214 to second waypoint 216. At second waypoint 216, aircraft 202 deviated from the planned flight path from second waypoint 216 to its current location via direct route 226, bypassing third waypoint 218. Direct route can include deviation from the planned flight path approved by the air traffic control system. Direct route can be granted for various reasons, including fuel consumption, air traffic management, passenger safety, etc.

[0054] As mentioned above Figure 1 As described, the terminal procedure prediction 122 may include a plurality of waypoints (e.g., waypoints 214, 216, 218), and the processor 106 may be configured to determine the likelihood of approval of a request for a direct route that bypasses one or more waypoints. In a particular aspect, the processor 106 is configured to analyze flight position data of historical flights and detect whether one or more waypoints were bypassed during the historical flights. As shown in the exemplary diagram 200, Figure 1 Processor 106 may be configured to analyze the flight position data for the first and second historical flights of aircraft 202 to detect that aircraft 202 bypassed third waypoint 218 during the second historical flight. Based on the analysis of the flight position data for the historical flights (and, in some configurations, additional data such as whether a direct route was requested, flight position data for historical flights of other aircraft along a flight path that included the same waypoint, etc.), processor 106 may determine the likelihood that a direct route is approved for a particular upcoming flight.

[0055] Figure 3 An exemplary architecture 300 for terminal procedure prediction for flight planning according to some implementations of the present disclosure is shown. The exemplary architecture 300 includes an input device 302 communicatively coupled to a processing device 304, an output device 306, one or more historical databases 308, one or more data sources 318 of contextual information, and one or more forecasters 328.

[0056] In general, the exemplary architecture 300 corresponds to Figure 1 For example, input device 302 generally corresponds to device 104, processing device 304 generally corresponds to processor 106, historical database generally corresponds to historical flight data 116, context information 318 generally corresponds to conditions 115, output from predictor 328 generally corresponds to prediction data 114, and output device 306 generally corresponds to terminal procedure predictor 120.

[0057] As referenced above Figure 1As described, a user, device, or some combination thereof may input a plurality of input data corresponding to conditions associated with an upcoming flight, including, in some aspects, date and time information 138, departure airport 140, arrival airport 142, aircraft type 144, aircraft configuration 146, etc. Figure 3 , data corresponding to conditions associated with an upcoming flight may be input at input device 302. In particular aspects, input device 302 may include any suitable electronic device configured to store input data, including a personal computer, laptop computer, smart phone, etc.; various input / output devices such as a keyboard, mouse, touch screen, etc.; data transmission from another electronic device, other suitable device for storing input data, or some combination thereof.

[0058] Once entered, the input device 302 can Figure 1 The input data 110 is transmitted to the processing device 304. The processing device 304 may be configured to process the input data 110 and the prediction data (e.g., Figure 1 The prediction data 114 is used to obtain a terminal procedure prediction associated with an upcoming flight (e.g., Figure 1 The processing device 304 may be included in any suitable electronic device of the terminal program prediction 122). For example, the processing device 304 may be included in Figure 1 The computing device 102 includes Figure 1 The computing device 102 and Figure 1 The computing device 102 shares functionality or otherwise communicates with Figure 1 The computing device 102 is associated with the device.

[0059] Processing device 304 may be configured to obtain information from one or more information sources, including from historical database 308. Figure 1 historical flight data 116 , conditions 115 from context information 318 , prediction data 114 from forecaster 328 , and the like.

[0060] In some implementations, each information source may include one or more sub-sources of information. For example, the historical database 308 may include a historical weather database 310, a database of position reports 312, a database of historical NOTAMs 314, a database of operational flight plans ("OFPs") 316, etc. The historical database 308 generally corresponds to Figure 1 Historical flight data of 116.

[0061] The context information 318 may include one or more navigation databases 320, a current weather database 322, a database of runways in use 324, a database of current NOTAMs 326, etc. The forecasters 328 may include an airport traffic forecaster 330 configured to provide an airport traffic model 154, a weather forecaster 332 configured to provide a weather forecast 152, a runway usage forecaster 334 configured to provide a runway prediction 150, etc.

[0062] In some implementations, processing device 304 may be configured to match actual and / or predicted data with historical condition data at block 336. For example, processing device 304 may compare input data 110, conditions 115, and historical flight data 116 with predicted data 114 from forecaster 328. Processing device 304 may be configured to identify, for example, Figure 1 A set of historical flights 128 that most closely match the characteristics of the upcoming flight.

[0063] At block 338, the processing device 304 may also be configured to obtain one or more terminal procedures that are most planned from the set of historical flights 128. In certain aspects, the most planned terminal procedures may be obtained from a database of the OFP 316. Once identified, the processing device 304 may also be configured to extract a plurality of waypoint (“WPT”) coordinates associated with the most planned terminal procedures at block 340. In certain aspects, the processing device 304 may obtain the waypoint coordinates from the navigation database 320.

[0064] The processing device 304 may also be configured to create one or more polygons associated with each waypoint at block 342, as described above with reference to FIG. Figure 2 304 may be further configured to match the reported aircraft position in the aircraft position report to the spatial limits of the waypoint polygon at block 344. The processing device 304 may also be configured to determine whether a number of position reports for a particular OFP satisfy a route threshold relative to the waypoint polygon at block 346. For example, the route threshold may be a predetermined percentage (e.g., 50%) of the waypoints of the aircraft report's associated waypoint polygon.

[0065] As mentioned above Figure 1As described, if the OFP meets the route threshold, then the output device 306 can be configured to output a predicted terminal procedure (e.g., terminal procedure prediction 122) at box 348, which includes a likelihood that the predicted terminal procedure will remain valid for the duration of the upcoming flight. Using the above embodiment, if the historical position report indicates that the aircraft passes within the majority of the waypoint polygon, then the output device 306 can be configured to output the predicted terminal procedure The likelihood that will remain valid. In a particular embodiment, the likelihood may be based on an analysis of multiple historical flights that meet the same route threshold criteria for a particular set of waypoints. If, in a group of fifty historical flights that include predicted terminal procedures, thirty of those historical flights meet the route threshold, then the output device 306 may include a 60% likelihood that the terminal procedure will remain valid for the duration of the upcoming flight.

[0066] If the OFP does not meet the route threshold, then the output device 306 can be configured to output the direct likelihood and location at block 350. Figure 2 As described, a direct approach may indicate that a particular historical flight bypassed one or more waypoints associated with the original OFP. By analyzing input data 110, historical flight data 116, conditions 115, and prediction data 114, processing device 304 may be configured to provide a probability that a particular flight will follow a certain terminal image, including the probability of a direct approach bypassing one or more portions of the original OFP.

[0067] In some implementations, processing device 304 may also be configured to update history database 308 with data associated with the upcoming flight. Processing device 304 may, for example, collect actual weather data associated with the planned flight, location information, actual runway data in use, actual airport traffic data, etc., while the flight is in progress. Processing device 304 may collect this data while the aircraft is in flight, collect this data from one or more data sources after the flight, or some combination thereof. Processing device 304 may be configured to then update history database 308 (and / or appropriate data sources) with the actual flight data obtained.

[0068] As post-flight data is updated with data from actual flights, databases of historical data (e.g., historical database 308) will have access to more accurate and robust data sets. By improving the available historical data sets, the exemplary architecture 300 (and Figure 1 The system 100 may improve the performance of obtaining the terminal program prediction 122 (e.g., by Figure 1 For example, when the training data 118 is based on a larger, more accurate set of historical flight data (e.g., Figure 1When the historical flight data 116 is updated, the trained machine learning model 124 can more accurately predict the terminal procedure. Continuously updating the historical flight data enables the systems and methods disclosed herein to continuously improve the terminal procedure prediction of the flight plan.

[0069] Figure 4 4 is a flow chart of an exemplary method 400 for terminal procedure prediction of a flight plan according to some embodiments of the present disclosure. The method 400 may be initiated, executed, or controlled by one or more processors executing instructions, such as a processor executing instructions from the memory 108. Figure 1 The processor 106 is started, executed or controlled by.

[0070] In some implementations, method 400 includes, at 402, obtaining, at a processor, input data corresponding to an upcoming flight of an aircraft. For example, Figure 1 The processor 106 can obtain Figure 2 The input data 110 corresponding to the upcoming flight of the aircraft 202 .

[0071] exist Figure 4 In an embodiment of the present invention, method 400 may further include, at 404, obtaining, at a processor, prediction data corresponding to an upcoming flight. For example, Figure 1 The processor 106 may obtain prediction data 114 corresponding to an upcoming flight.

[0072] exist Figure 4 In an embodiment of the present invention, method 400 may further include, at 406, processing the input data and the prediction data at the processor to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data. For example, processor 106 may process input data 110 and prediction data 114 to obtain a terminal procedure prediction 122 associated with the upcoming flight, the terminal procedure prediction 122 being based at least in part on flight plans 112 of historical flights having conditions similar to input data 110 and prediction data 114.

[0073] Although method 400 is illustrated as including a certain number of steps, more, fewer, and / or different steps may be included in method 400 without departing from the scope of the present disclosure. For example, method 400 may optionally include additional steps, as described in more detail below.

[0074] exist Figure 4 In an embodiment of the present invention, method 400 may optionally include, at 408, obtaining aviation notification information, wherein the terminal procedure prediction is further based on the aviation notification information. Figure 1 The processor 106 may be configured to Figure 3 The aeronautical notification information may be obtained from a database of historical NOTAMs 314, a database of current NOTAMs 326, or some combination thereof. The processor 106 may be configured to further base the terminal procedure prediction 122 on the aeronautical notification information.

[0075] exist Figure 4 In an embodiment of the method 400 may optionally include, at 410, determining a likelihood of approval of a request for a direct route that bypasses one or more waypoints included in the terminal program prediction. For example, Figure 1 The processor 106 may be configured to determine the likelihood of approval of a request for a direct route that bypasses one or more waypoints included in the terminal program prediction 122, as described above with reference to Figures 2 to 3 as described.

[0076] exist Figure 4 In an embodiment of the present invention, the method 400 may also optionally include, at 412, analyzing the flight position data of the historical flight and detecting whether one or more waypoints were bypassed during the historical flight. Figure 3 The processing device may be configured to analyze the flight position data from the database of position reports 312 and detect whether one or more waypoints were bypassed during historical flights, as described above with reference to Figures 2 to 3 as described.

[0077] exist Figure 4 In an embodiment of the present invention, the method 400 may optionally include, at 414, determining a fuel safety margin based on a flight plan of the upcoming flight including the terminal procedure prediction. For example, the processor 106 may be configured to determine a fuel safety margin based on a flight plan of the upcoming flight including the terminal procedure prediction 122.

[0078] As described above, more, fewer, and / or different steps may be included in method 400 without departing from the scope of the present disclosure. For example, method 400 may vary depending on the count and type of input data received. In a particular configuration, Figure 1 The system 100 may be configured to obtain a preliminary terminal program prediction 122 if incomplete input data 110 is obtained, and then update the terminal program prediction 122 when additional input data 110 is received.

[0079] Figure 5 is a block diagram of a computing environment 500 including a computing device 510 configured to support various aspects of computer-implemented methods and computer-executable program instructions (or codes) according to some embodiments of the present disclosure. For example, the computing device 510 or a portion thereof is configured to execute instructions to initiate, execute, or control the above-referenced Figures 1 to 4In certain aspects, computing device 510 may include, correspond to, or include Figure 1 computing device 102, device 104, Figure 3 The input device 302, the processing device 304, the output device 306, the historical database 308, the context information 318, the forecaster 328, one or more servers, one or more virtual devices, or a combination thereof.

[0080] The computing device 510 includes one or more processors 520. In certain aspects, the processor 520 corresponds to Figure 1 106. The processor 520 is configured to communicate with a system memory 530, one or more storage devices 550, one or more input / output interfaces 540, one or more communication interfaces 560, or any combination thereof. The system memory 530 includes volatile memory devices (e.g., random access memory (RAM) devices), non-volatile memory devices (e.g., read-only memory (ROM) devices, programmable read-only memory, and flash memory), or both. The system memory 530 stores an operating system 532, which may include a basic input / output system for booting the computing device 510 and a complete operating system that enables the computing device 510 to interact with users, other programs, and other devices. The system memory 530 stores system (program) data 538, such as Figure 1 prediction data 114, conditions 115, historical flight data 116, or a combination thereof.

[0081] System memory 530 contains one or more applications 534 (eg, instruction sets) that are executable by processor 520. For example, one or more applications 534 include instructions 536 that are executable by processor 520 to initiate, control, or execute a reference Figures 1 to 4 For illustration, one or more applications 534 include instructions 536 executable by processor 520 to initiate, control, or perform one or more operations described with reference to processing input data 110 and prediction data 114 to obtain a terminal procedure prediction 122 associated with an upcoming flight, wherein the terminal procedure prediction 122 is based at least in part on flight plans 112 of historical flights having conditions similar to the input data 110 and prediction data 114.

[0082] In a particular implementation, the system memory 530 includes a non-transitory computer-readable medium (e.g., a computer-readable storage device) storing instructions 536 that, when executed by the processor 520, cause the processor 520 to initiate, perform, or control operations to improve the automated environment of aviation information services. The operations include obtaining, at the processor, input data corresponding to an upcoming flight of an aircraft. The operations also include obtaining, at the processor, prediction data corresponding to the upcoming flight. The operations also include processing, at the processor, the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on a flight plan of a historical flight having conditions corresponding to the input data and the prediction data.

[0083] One or more storage devices 550 include non-volatile storage devices, such as magnetic disks, optical disks, or flash memory devices. In certain embodiments, storage devices 550 include both removable and non-removable memory devices. Storage devices 550 are configured to store operating systems, images of operating systems, applications (e.g., one or more applications 534), and program data (e.g., program data 538). In certain aspects, system memory 530, storage devices 550, or both contain tangible computer-readable media. In certain aspects, one or more storage devices 550 are external to computing device 510.

[0084] One or more input / output interfaces 540 enable computing device 510 to communicate with one or more input / output devices 570 to facilitate user interaction. For example, one or more input / output interfaces 540 may include a display interface, an input interface, or both. For example, input / output interface 540 is suitable for receiving input from a user, receiving input from another computing device, or a combination thereof. In some implementations, input / output interface 540 complies with one or more standard interface protocols, including a serial interface (e.g., a universal serial bus (USB) interface or an Institute of Electrical and Electronics Engineers (IEEE) interface standard), a parallel interface, a display adapter, an audio adapter, or a custom interface ("IEEE" is a registered trademark of the State of New Jersey, Piscataway, Inc., the Institute of Electrical and Electronics Engineers). In some implementations, input / output device 570 includes one or more user interface devices and displays, including some combinations of buttons, keyboards, pointing devices, displays, speakers, microphones, touch screens, and other devices.

[0085] The processor 520 is configured to communicate with a device or controller 580 via one or more communication interfaces 560. For example, the one or more communication interfaces 560 may include a network interface. The device or controller 580 may include, for example Figure 1 device 104.

[0086] In some implementations, a non-transitory computer-readable medium (e.g., a computer-readable storage device) stores instructions that, when executed by one or more processors, cause the one or more processors to initiate, perform, or control operations to perform some or all of the functions described above. For example, the instructions may be executed to implement Figure 1 , Figure 3 and Figure 4 In some implementations, the instructions may be implemented by one or more processors (e.g., one or more central processing units (CPUs), one or more graphics processing units (GPUs), one or more digital signal processors (DSPs)) executing instructions, by dedicated hardware circuits, or any combination thereof. Figure 1 , Figure 3 and Figure 4 One or more operations or methods may be performed in part or in whole.

[0087] In conjunction with the described implementations, an apparatus includes means for obtaining, at a processor, input data corresponding to an upcoming flight of an aircraft. For example, the means for obtaining the input data includes Figure 1 computing device 102, processor 106, device 104, processor 132, Figure 3 historical database 308 , context information 318 , input device 302 , processing device 304 , one or more other circuits or components configured to obtain input data corresponding to an upcoming flight of an aircraft at the processor, or any combination thereof.

[0088] The apparatus also includes means for obtaining, at the processor, prediction data corresponding to an upcoming flight. For example, the means for obtaining prediction data includes Figure 1 computing device 102, processor 106, device 104, processor 132, Figure 3 The predictor 328, the input device 302, the processing device 304, one or more circuits or components configured to obtain prediction data corresponding to an upcoming flight at the processor, or any combination thereof.

[0089] The apparatus also includes means for processing the input data and the predicted data at the processor to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the predicted data. The means for processing the input data and the predicted data includes Figure 1 computing device 102, processor 106, device 104, processor 132, Figure 3a processing device 304, one or more circuits or components configured to process the input data and the predicted data at a processor to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the predicted data, or any combination thereof.

[0090] The illustrations of the embodiments described herein are intended to provide a general understanding of the structures of different implementations. These illustrations are not intended to serve as a complete description of all elements and features of the devices and systems utilizing the structures or methods described herein. After reviewing this disclosure, many other implementations may be apparent to those skilled in the art. Other implementations may be utilized and derived from this disclosure so that structural and logical replacements and changes may be made without departing from the scope of this disclosure. For example, method operations may be performed in an order different from the order shown in the figure or one or more method operations may be omitted. Therefore, this disclosure and the accompanying drawings are considered to be illustrative and not restrictive.

[0091] In addition, although specific embodiments have been shown and described herein, it should be understood that any subsequent arrangement designed to achieve the same or similar results may replace the specific implementation shown. This disclosure is intended to cover any and all subsequent adaptations or variations of different implementations. Combinations of the above implementations and other implementations not specifically described herein will be apparent to those skilled in the art upon reviewing this description.

[0092] Submitting an abstract of the present disclosure, it should be understood that it is not used to interpret or limit the scope or meaning of the claims. In addition, in the aforementioned specific implementations, for the purpose of streamlining the present disclosure, different features can be combined together or described in a single implementation. The above embodiments illustrate but do not limit the present disclosure. It should also be understood that many modifications and variations are feasible according to the principles of the present disclosure. As reflected in the attached claims, the subject matter claimed for protection may be directed to features less than all the features of any disclosed embodiment. Therefore, the scope of the present disclosure is limited by the attached claims and their equivalents.

[0093] In addition, the present disclosure includes implementations according to the following examples:

[0094] According to embodiment 1, a device includes: one or more processors configured to obtain input data corresponding to an upcoming flight of an aircraft; obtain prediction data corresponding to the upcoming flight; and process the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data.

[0095] Embodiment 2 comprises the apparatus according to embodiment 1, wherein one or more processors are configured to process input data and prediction data using a trained machine learning model.

[0096] Embodiment 3 comprises the apparatus according to embodiment 1 or embodiment 2, wherein the trained machine learning model is trained based on historical flights.

[0097] Embodiment 4 includes the apparatus of any of embodiments 1 to 3, wherein the one or more processors are configured to access a database of historical flights to identify a set of historical flights based on one or more comparisons of the input data and the predicted data with conditions of the historical flights.

[0098] Embodiment 5 includes the apparatus of embodiment 4, wherein the terminal procedure prediction is obtained based on a statistical analysis of the set of historical flights.

[0099] Embodiment 6 includes the apparatus of any of embodiments 1 to 5, wherein the input data includes at least one of date and time information, departure airport, arrival airport, aircraft type, aircraft configuration, and aircraft weight and balance data.

[0100] Embodiment 7 includes the apparatus of any one of embodiments 1 to 6, wherein the prediction data includes at least one of a runway prediction, a weather forecast, and an airport traffic model.

[0101] Embodiment 8 includes the apparatus of any one of embodiments 1 to 7, wherein the one or more processors are further configured to obtain aviation notification information, and wherein the terminal procedure prediction is further based on the aviation notification information.

[0102] Embodiment 9 includes the apparatus of any of embodiments 1 to 8, wherein the terminal procedure prediction includes a plurality of waypoints, and wherein the one or more processors are further configured to determine a likelihood of approval of a request for a direct route that bypasses one or more waypoints.

[0103] Embodiment 10 includes the apparatus of embodiment 9, wherein the one or more processors are configured to analyze flight position data of historical flights and detect whether one or more waypoints were bypassed during the historical flights.

[0104] Embodiment 11 includes the apparatus of any of embodiments 1 to 10, wherein the one or more processors are configured to determine the fuel safety margin based on a flight plan for an upcoming flight including a terminal procedure prediction.

[0105] Embodiment 12 includes the apparatus of any of embodiments 1 to 11, wherein the apparatus is included in a flight dispatch system, and wherein the one or more processors are configured to submit the terminal procedure prediction to an air traffic control approval system.

[0106] Embodiment 13 includes an apparatus according to any of Embodiments 1 to 12, wherein the apparatus is included in an air traffic control approval system and wherein the one or more processors are configured to compare the terminal procedure prediction with a flight plan for an upcoming flight that has been submitted to the air traffic control approval system.

[0107] According to embodiment 14, a method includes: obtaining input data corresponding to an upcoming flight of an aircraft at a processor; obtaining prediction data corresponding to the upcoming flight at the processor; and processing the input data and the prediction data at the processor to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data.

[0108] Embodiment 15 comprises the method according to embodiment 14, wherein a trained machine learning model is used to process input data and predict data.

[0109] Embodiment 16 includes the method of embodiment 14 or embodiment 15, wherein processing the input data and the predicted data includes accessing a database of historical flights to identify a set of historical flights based on one or more comparisons of the input data and the predicted data to conditions of the historical flights.

[0110] Embodiment 17 includes the method of any of Embodiments 14 to 16, wherein the input data includes at least one of date and time information, departure airport, arrival airport, aircraft type, aircraft configuration, and aircraft weight and balance data.

[0111] Embodiment 18 comprises the method of any one of Embodiments 14 to 17, wherein the prediction data comprises at least one of a runway prediction, a weather forecast, and an airport traffic model.

[0112] Embodiment 19 includes the method of any of Embodiments 14 to 18, and further includes determining a likelihood of approving a request for a direct route that bypasses one or more waypoints included in the terminal procedure prediction.

[0113] According to embodiment 20, a non-transitory computer-readable medium includes instructions that, when executed by one or more processors, cause the one or more processors to: obtain input data corresponding to an upcoming flight of an aircraft; obtain prediction data corresponding to the upcoming flight; and process the input data and the prediction data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans of historical flights having conditions similar to the input data and the prediction data.

[0114] Embodiment 21 includes a non-transitory computer-readable medium according to embodiment 20, wherein the instructions, when executed by a processor, cause one or more processors to be configured to process input data and prediction data using a trained machine learning model.

[0115] Embodiment 22 includes a non-transitory computer-readable medium according to embodiment 20 or embodiment 21, wherein the trained machine learning model is trained based on historical flights.

[0116] Embodiment 23 includes a non-transitory computer-readable medium according to any of Embodiments 20 to 22, wherein the instructions, when executed by a processor, cause one or more processors to access a database of historical flights to identify a set of historical flights based on one or more comparisons of input data and predicted data with conditions of the historical flights.

[0117] Embodiment 24 includes the non-transitory computer readable medium of embodiment 23, wherein the terminal procedure prediction is obtained based on a statistical analysis of the set of historical flights.

[0118] Embodiment 25 includes a non-transitory computer readable medium according to any one of embodiments 20 to 24, wherein the input data includes at least one of date and time information, departure airport, arrival airport, aircraft type, aircraft configuration, and aircraft weight and balance data.

[0119] Embodiment 26 includes the non-transitory computer readable medium of any one of Embodiments 20 to 25, wherein the prediction data includes at least one of a runway prediction, a weather forecast, and an airport traffic model.

[0120] Embodiment 27 includes a non-transitory computer readable medium according to any of embodiments 20 to 26, wherein the instructions, when executed by a processor, cause the one or more processors to obtain avionic notification information, and wherein the terminal procedure prediction is further based on the avionic notification information.

[0121] Embodiment 28 includes a non-transitory computer-readable medium according to any of embodiments 20 to 27, wherein the terminal program prediction includes a plurality of waypoints, and wherein the instructions, when executed by the processor, cause the one or more processors to determine the likelihood of approval of a request for a direct route that bypasses one or more waypoints.

[0122] Embodiment 29 includes a non-transitory computer readable medium according to embodiment 28, wherein the instructions, when executed by a processor, cause the one or more processors to analyze flight position data of historical flights and detect whether one or more waypoints were bypassed during the historical flights.

[0123] Embodiment 30 includes a non-transitory computer readable medium according to any of Embodiments 20 to 29, wherein the instructions, when executed by a processor, cause one or more processors to determine a fuel safety margin based on a flight plan for an upcoming flight including a terminal procedure prediction.

[0124] Embodiment 31 includes a non-transitory computer-readable medium according to any one of embodiments 20 to 30, wherein the non-transitory computer-readable medium is included in a flight dispatch system, and wherein the instructions, when executed by a processor, cause one or more processors to submit a terminal procedure prediction to an air traffic control approval system.

[0125] Embodiment 32 comprises a non-transitory computer-readable medium according to any of Embodiments 20 to 31, wherein the non-transitory computer-readable medium is included in an air traffic control approval system, and wherein the instructions, when executed by a processor, cause one or more processors to compare a terminal procedure prediction with a flight plan for an upcoming flight that has been submitted to the air traffic control approval system.

[0126] According to embodiment 33, a device includes: a device for obtaining input data corresponding to an upcoming flight of an aircraft at a processor; a device for obtaining predicted data corresponding to the upcoming flight at the processor; and a device for processing the input data and the predicted data at the processor to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction being based at least in part on a flight plan of a historical flight having conditions similar to the input data and the predicted data.

[0127] Embodiment 34 comprises the apparatus according to embodiment 33, wherein a trained machine learning model is used to process input data and predict data.

[0128] Embodiment 35 comprises an apparatus according to embodiment 33 or embodiment 34, wherein the means for processing input data and predicted data comprises means for accessing a database of historical flights to identify a set of historical flights based on one or more comparisons of the input data and predicted data with conditions of the historical flights.

[0129] Embodiment 36 includes an apparatus according to any one of Embodiments 33 to 35, wherein the input data includes at least one of date and time information, departure airport, arrival airport, aircraft type, aircraft configuration, and aircraft weight and balance data.

[0130] Embodiment 37 includes an apparatus according to any one of Embodiments 33 to 36, wherein the prediction data includes at least one of a runway prediction, a weather forecast, and an airport traffic model.

[0131] Embodiment 38 includes the apparatus of any of Embodiments 33 to 37, and further includes means for determining a likelihood of approval of a request for a direct route that bypasses one or more waypoints included in the terminal procedure prediction.

Claims

1. A device (102) for terminal procedure prediction of a flight plan, the device comprising: One or more processors (106) configured to: obtaining input data corresponding to an upcoming flight of an aircraft (110); obtaining prediction data corresponding to the upcoming flight (114); and The input data and the predicted data are processed to obtain a terminal procedure prediction (122) associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans (112) of historical flights having conditions (115) similar to the input data and the predicted data.

2. The device according to claim 1, wherein The one or more processors are configured to process the input data and the prediction data using a trained machine learning model (124).

3. The device according to claim 2, wherein: The trained machine learning model is trained (118) based on historical flights (116).

4. The device according to claim 1, wherein: The one or more processors are configured to access the database of historical flights to identify a set of historical flights (128) based on one or more comparisons of the input data and the predicted data with conditions (130) of the historical flights.

5. The device according to claim 4, wherein: The terminal procedure prediction is obtained based on a statistical analysis of the set of historical flights.

6. The device according to claim 1, wherein: The input data includes at least one of date and time information (138), departure airport (140), arrival airport (142), aircraft type (144), aircraft configuration (146), and aircraft weight and balance data (148).

7. The device according to claim 1, wherein: The prediction data includes at least one of a runway prediction (150), a weather forecast (152), and an airport traffic model (154).

8. The device according to claim 1, wherein: The one or more processors are further configured to obtain aviation notification information (314, 326), and wherein the terminal procedure prediction is further based on the aviation notification information.

9. The device according to claim 1, wherein: The terminal procedure prediction includes a plurality of waypoints (214, 216, 218), and wherein the one or more processors are further configured to determine a likelihood of approval of a request for a direct route (226) that bypasses the one or more waypoints.

10. The device according to claim 9, wherein: The one or more processors are configured to analyze flight position data (312) of the historical flight and detect whether the one or more waypoints were bypassed during the historical flight.

11. The device according to claim 1, wherein: The one or more processors are configured to determine a fuel safety margin based on a flight plan (112) for the upcoming flight including the terminal procedure prediction.

12. The device according to claim 1, wherein: The apparatus is included in a flight dispatch system, and wherein the one or more processors are configured to submit the terminal procedure prediction to an air traffic control approval system.

13. The apparatus according to claim 1, wherein: The apparatus is included in an air traffic control approval system, and wherein the one or more processors are configured to compare the terminal procedure prediction to a flight plan for the upcoming flight that has been submitted to the air traffic control approval system.

14. A method (400) for terminal procedure prediction of a flight plan, the method comprising: obtaining (402) at a processor (106, 132) input data (110) corresponding to an upcoming flight of an aircraft; obtaining (404) at the processor predicted data (114) corresponding to the upcoming flight; as well as The input data and the predicted data are processed (406) at the processor to obtain a terminal procedure prediction (122) associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans (112) of historical flights having conditions (115) similar to the input data and the predicted data.

15. The method according to claim 14, wherein: The input data and the predicted data are processed using a trained machine learning model (124).

16. The method according to claim 14, wherein: Processing the input data and the predicted data includes accessing a database of historical flights (116) to identify a set of historical flights (128) based on one or more comparisons of the input data and the predicted data to conditions (130) of the historical flights.

17. The method according to claim 14, wherein: The input data includes at least one of date and time information (138), departure airport (140), arrival airport (142), aircraft type (144), aircraft configuration (146), and aircraft weight and balance data (148).

18. The method according to claim 14, wherein: The prediction data includes at least one of a runway prediction (150), a weather forecast (152), and an airport traffic model (154).

19. The method according to claim 14, further comprising: A likelihood of approval of a request for a direct route (226) that bypasses one or more waypoints (214, 216, 218) included in the terminal procedure prediction is determined.

20. A non-transitory computer-readable medium (530, 108, 134) comprising instructions (536) that, when executed by one or more processors (106, 132), cause the one or more processors to: obtaining input data corresponding to an upcoming flight of an aircraft (110); obtaining prediction data corresponding to the upcoming flight (114); and The input data and the predicted data are processed to obtain a terminal procedure prediction (122) associated with the upcoming flight, the terminal procedure prediction being based at least in part on flight plans (112) of historical flights having conditions (115) similar to the input data and the predicted data.

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