Claims:1. A system configured to predict an estimated time of arrival (ETA) of a flight, the system comprises:
at least one memory storing a plurality of instructions;
one or more hardware processors communicatively coupled with the at least one memory, wherein the one or more hardware processors are configured to execute one or more modules;
a receiving module configured to receive estimated on time (EoN) information of the flight as an input to the system, wherein the EoN information includes a taxi-out data of the flight, an airtime data of the flight, and a real time weather data, wherein taxi-out time is defined as time between the actual pushback and takeoff of the flight, and the air time of the flight includes total time from the moment that an aircraft first moves under its own power for the purpose of taking off until the moment the aircraft comes to rest at the end of the flight;
an operation characterization module configured to analyze one or more operational scenarios considering one or more dimensional aspects and the received input to the system, wherein the one or more operational scenarios includes fleet deployment pattern, network flow characterization, and operational preferences;
a learning module configured to learn the analyzed one or more operational scenarios, wherein the learning of one or more operational scenarios defines one or more operational levers, wherein the one or more operational levers include needs of a crew, operations of one or more gates, and a rescheduling priorities;
a determining module configured to determine taxi-in time of the flight using a moving average (MA) model, wherein the taxi-in time is defined as time between a wheels-on time and a gate-in time, wherein the MA model is used to learn the behavior of the taxi-in time of the flight;
a simulation module configured to simulate the received EoN information of the flight and the determined taxi-in time of the flight; and
a prediction module configured to predict the estimated time of arrival (ETA) of the flight using the simulated result of EoN information of the flight and the determined taxi-in time of the flight.

2. The system claimed in claim 1, wherein the one or more dimensional aspects include origin destination pairs and connections, flight frequency, operational delay classification, clock times, fleet types, operational crew data and network model.

3. The system claimed in claim 1, wherein the air time of the flight depends on a congested airspace, weather, traffic control actions, and a type of the aircraft.

4. The system claimed in claim 1, wherein the taxi-out time and taxi-in time of the flight depends on a runway configuration, downstream restrictions, and an arrival queue.

5. The system claimed in claim 1, wherein the moving average model is used to determine one or more standard deviation of the taxi-in time of the flight for a normal distribution having one or more accuracy levels for a specific time interval.

6. A processor-implemented method to predict an estimated time of arrival (ETA) of a flight, the processors- implemented method comprising one or more steps of:
receiving, via the one or more hardware processors, estimated on time (EoN) information of the flight as an input to the system, wherein the EoN information includes a taxi-out data of the flight, an airtime data, and a real time weather data, wherein taxi-out time is defined as time between the actual pushback and takeoff of the flight, and the air time of the flight includes total time from the moment that an aircraft first moves under its own power for the purpose of taking off until the moment the aircraft comes to rest at the end of the flight;
analyzing, via the one or more hardware processors, one or more operational scenarios at an operation characterization module of the system considering one or more dimensional aspects and the received input, wherein the one or more operational scenarios includes fleet deployment pattern, network flow characterization, and operational preferences;
learning, via the one or more hardware processors, the analyzed one or more operational scenarios at a learning module of the system, wherein the learning of one or more operational scenarios defines one or more operational levers, wherein the one or more operational levers include needs of a crew, operations of one or more gates, and a rescheduling priorities;
determining, via the one or more hardware processors, taxi-in time of the flight using a moving average (MA) model, wherein the taxi-in time is defined as time between a wheels-on time and a gate-in time, wherein the MA model is used to learn the behavior of the taxi-in time of the flight;
simulating, via the one or more hardware processors, the received EoN information of the flight and the determined taxi-in time of the flight to predict the estimated time of arrival (ETA) of the flight; and
predicting, via the one or more hardware processors, estimated time of arrival (ETA) of the flight using result of simulation of the EoN information and the determined taxi-in time of the flight.

7. The method claimed in claim 6, wherein the one or more dimensional aspects include origin destination pairs and connections, flight frequency, operational delay classification, clock times, fleet types, operational crew data and network model.

8. The method claimed in claim 6, wherein the air time of the flight depends on a congested airspace, weather, traffic control actions, and a type of the aircraft.

9. The method claimed in claim 6, wherein the taxi-out time and taxi-in time of the flight depends on a runway configuration, downstream restrictions, and an arrival queue.

10. The method claimed in claim 6, wherein the moving average model is used to determine one or more standard deviation of the taxi-in time of the flight for a normal distribution having one or more accuracy levels for a specific time interval.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

SYSTEM AND METHOD FOR PREDICTING ESTIMATED TIME OF ARRIVAL OF A FLIGHT

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The disclosure herein generally relates to the field of civil aviation, and, more particularly, but not specifically, a system and method for predicting estimated time of arrival (ETA) of a flight using a moving average (MA) model.

BACKGROUND
[002] In the field of civil aviation, flight time prediction is a process of calculating an expected flight delay in advance. The expected flight delay includes expected time of arrival (ETA), expected time of departure (ETD) as well airlines operations such as taxi-in, taxi-out and airtime of the aircraft. It has been observed that delays in the scheduled departure times at the gates in origin airports as well as from the scheduled arrival times at the gates in the destination airports are quite frequent in domestic and international flights. These delays are a major source of frustration and cost for the passengers as well airlines. Further, the delay and disruption costs account to about 8% of airline revenues. It is to be noted that the flights are more frequently delayed due to weather, airport and airlines operations, congestion, crew connectivity, passenger connectivity and so on, and there has been continual interest and potential motivation shown by airlines across the world on estimation and prediction of accurate flight departure times and arrival time information to their passenger as well as travelers. The delays are typically a stochastic phenomenon. Therefore, it is needed to analyze their entire probability distributions.

[003] It should be appreciated that the taxi-out time is defined as the time between the actual push-back and takeoff. This is the time that the aircraft spends on the airport surface with engines on, and includes the time spent on the taxiway system and in the runway queues. Further, the air time is defined as the total time from the moment that an aircraft first moves under its own or external power for the purpose of taking off until the moment it comes to rest at the end of the flight. Further, the Airtime is also called as flight time. Both operations like taxi-out and Airtime combine together producing Estimated on time (EoN) of a flight. This EoN happens before taxi-in operation begins.

[004] Conventional method(s) and system(s) for flight ETA prediction are predicting individual factors and there is no holistic arrangement to calculate the ETA prediction by utilizing all the possible parameters pertaining to an airlines system. Moreover, since the conventional methods are utilizing less number of parameters, the accuracy is less.

SUMMARY
[005] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a system configured to predict estimated time of arrival (ETA) of a flight is provided. The ETA prediction using a simulation of computed taxi-in time and received estimated on time (hereinafter read as EoN) information of the flight.

[006] The system includes at least one memory with a plurality of instructions and one or more hardware processors communicatively coupled with the at least one memory to execute one or more modules. Further, the system comprises a receiving module that is configured to receive EoN information of the flight, wherein the EoN information includes a taxi-out data of the flight, an airtime data of the flight, and a real time weather data, as an input to the system. An operation characterization module of the system is configured to analyze one or more operational scenarios considering one or more dimensional aspects and the received input to the system, wherein the one or more operational scenarios includes fleet deployment pattern, network flow characterization, and operational preferences.

[007] Further herein, a learning module of the system is configured to learn the analyzed one or more operational scenarios, wherein the learning of operational scenarios defines one or more operational levers, wherein the one or more operational levers include needs of a crew, operations of one or more gates, and a rescheduling priorities, a determination module configured to determine taxi-in time of the flight using a moving average (MA) model, wherein the taxi-in time is defined as time between a wheels-on time and a gate-in time, wherein the MA model is used to learn the behavior of the taxi-in time of the flight, a simulation module of the system is configured to simulate the received EoN information of the flight and the determined taxi-in time of the flight and finally a prediction module of the system is configured to predict the estimated time of arrival (ETA) of the flight.

[008] In another aspect, a processor-implemented method to predict flight delay is provided. The method includes one or more steps such as receiving EoN information of the flight at a receiving module of the system, wherein the EoN information includes a taxi-out data of the flight, an airtime data of the flight, and a real time weather data, as input to the system, analyzing one or more operational scenarios at an operation characterization module of the system considering one or more dimensional aspects and the received input, wherein the one or more operational scenarios includes fleet deployment pattern, network flow characterization, and operational preferences.

[009] Further, the method includes learning of the analyzed one or more operational scenarios at a learning module of the system. Wherein the learning of operational scenarios defines one or more operational levers. It would be appreciated that the one or more operational levers include needs of a crew, operations of one or more gates, and a rescheduling priorities. Further the process includes determining taxi-in time of the flight at a determining module using a moving average (MA) model, wherein the taxi-in time is defined as time between a wheels-on time and a gate-in time, wherein the MA model is used to learn the behavior of the taxi-in time of the flight. Further, the method includes simulating the received EoN information and the determined taxi-in time of the flight at a simulation module and finally predicting the estimated time of arrival (ETA) of the flight using the result of simulation of the EoN information and the determined taxi-in time of the flight.

[010] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[011] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:

[012] FIG. 1 illustrates an exemplary system to predict estimated time of arrival of a flight, according to some embodiments of the present disclosure;

[013] FIG. 2 is a schematic architecture to explain ETA prediction according to an embodiments of the present disclosure; and

[014] FIG. 3 is a flow diagram to illustrate a method to predict an estimated time of arrival of a flight in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[015] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.

[016] Referring now to the drawings, and more particularly to FIG. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.

[017] Referring FIG. 1, a system (100) is configured to predict estimated time of arrival (ETA) of a flight. The system (100) executes the prediction by simulating EoN information and a determined taxi-in time of the flight along with historical operations (arrival and departure) data, historical airport data (captured at the time of arrival and departure) including congestion information, weather data, etc. Further, the system (100) is configured to analyze one or more operational scenarios considering one or more dimensional aspects and the received input to the system. The system (100) is also configured to categorize input data related to airline history, airline network, airport data and various airlines reference data.

[018] It would be appreciated that the taxi-in time determination uses a moving average (MA) model for data analytics. The moving average model is a commonly used model to understand the behavior of the data flow. Generally, the behavior of data flow fluctuates wildly over time due to the frequent change in data attributes. This fluctuation makes interpretation of the data flow difficult. The moving average is usually taken by averaging the operational data over a period of time producing a smoother line.

[019] In the preferred embodiment, the system (100) comprises at least one memory (102) with a plurality of instructions and one or more hardware processors (104) which are communicatively coupled with the at least one memory (102) to execute modules therein.

[020] The hardware processor (104) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the hardware processor (104) is configured to fetch and execute computer-readable instructions stored in the memory (102). Further, the system comprises a receiving module (106), an operation characterization module (108), a learning module (110), a determination module (112), a simulation module (114) and a prediction module (116).

[021] In the preferred embodiment, a receiving module (106) of the system (100) is configured to receive EoN information of the flight, wherein the EoN information includes a taxi-out data of the flight, an airtime data of the flight, and a real time weather data as an input to the system (100).

[022] The taxi-out time is defined as time between the actual pushback and takeoff of the flight. This is the time that the aircraft spends on the airport surface with engines on, and includes the time spent on the taxiway system and in the runway queues. It would be appreciated that the surface emissions from departures are therefore closely linked to the taxi-out times.

[023] The air time of the flight includes total time from the moment that an aircraft first moves under its own power for the purpose of taking off until the moment the aircraft comes to rest at the end of the flight. The air time of the flight depends on one or more factors such as a congested airspace, weather, traffic control actions, and a type of the aircraft.

[024] Further herein, the receiving module (106) of the system (100) is configured to receive the set of airline data including a flight data, a sector data, a movement data, an aircraft data, a navigation data, an air conditioner (A/C) performance data, a set of rules and policies data, and an airport data. The flight data includes towing, zero fuel weight, expected time of departure (ETD), route selection, preferred route, flight level, cost option, fuel parameters, point of beginning, and performance schedules. The sector data includes holding, taxi, alternate airports, restrictions, congestion. The movement data includes OOOI Data (gate out, wheels Off, wheels On, gate In), time, fuel, waypoints data, path deviation. The aircraft data includes max weights, cruise schedules (actuals). The weather data includes wind direction, speed, temperature, weather events. The navigation data includes route, waypoints, airways, standard instrument departure (SID), standard terminal arrival route (STAR). The A/C performance data includes true air speed, fuel flow, and range. The set of rules and policies includes company information, policies and procedures. The airport data includes runway, declaration of performance, key performance indicators (KPIs), resources, gate info, and flight schedules congestion.

[025] In the preferred embodiment, an operation characterization module (108) of the system (100) is configured to analyze one or more operational scenarios considering one or more dimensional aspects and the received input to the system (100). The one or more operational scenarios includes fleet deployment pattern, network flow characterization, and operational preferences. The one or more dimensional aspects include origin destination pairs and connections, a flight frequency, an operational delay classification, clock times, fleet types, an operational crew data and a network model. The one or more operational scenarios are utilized to ensure whether an outcome is capable of enabling a decision maker to define operational priorities and levers to manage delays and disruptions.

[026] The operation characterization module (108) utilizes the historical operations data (for example, D0/D15/D30 inputs, min/max/ave block times, taxi times, ON/OFF/other statistics), the reference Data (for example, Airport gate information, runway information) and the planning data (for example, Markets, i.e., origin destination pairs, Cost inputs, operational fleet information) to analyze and define a set of operational scenarios. The analysis is based on a plurality of dimensional aspects not limiting to origin destination pairs and connections, the flight frequency, the operational delay classification, clock times, fleet types, Operations crew data, and network models (for example, point to point/hub and spoke/mixed). The set of operational scenarios typically include extracting and identifying fleet deployment patterns, network flow characterization, and operational preferences. The set of operational scenarios are utilized to ensure whether an outcome is capable of enabling a decision maker to define operational priorities and levers to manage delays and disruptions.

[027] In the preferred embodiment, a learning module (110) of the system (100) is configured to learn the analyzed one or more operational scenarios. The learning of operational scenarios defines one or more operational levers. Herein, the one or more operational levers include needs of a crew, operations of one or more gates, and a rescheduling priorities.

[028] It would be appreciated that the learning module (110) of the system (100) comprises a set of models to analyze one or more operational scenarios of the airport and airlines. The set of models includes a set of basic statistical models, a set of advanced statistical models, a probabilistic graphical model, a stochastic simulation model, a set of critical path methods and an impact analysis model. The set of advanced statistical models includes but not limited to cluster model, fleet profiling models and diffusion models.

[029] In addition to this, there are a set of parameters, affecting the airlines journey, include an en-route time, an inbound gate time, a turnaround time and an outbound gate time. A set of factors affecting the en-route time includes weather, type of the aircraft, restrictions and network congestion. A set of factors affecting the inbound gate time includes an airport congestion, a runway type, a gate availability, a distance between runway and gate, a wind direction and a runway location, an aircraft marshalling, an ACFT type and gate restrictions. A set of factors affecting the turnaround time includes baggage offloading, baggage on-loading, cargo offloading, cargo on-loading, routine check, maintenance, pilot error report, crew offloading, crew on-loading, gate jet stream, passenger offloading, passenger on-loading, fuelling, APU and Pre-arrival tasks. A set of factors affecting outbound gate time includes distance between gate and runway, airport congestion and wind direction and runway location.

[030] In the preferred embodiment, a determination module (112) of the system (100) is configured to determine the taxi-in time of the flight using the moving average (MA) model. The MA model is used to learn the behavior of the taxi-in time of the flight. The taxi-in time is defined as time between a wheels-on time and a gate-in time. The taxi-in time is analyzed with a decision tree model to obtain one or more prediction values. The obtained one or more prediction values are filtered out for an individual flight.

[031] In the preferred embodiment, the taxi-in time is defined as time between a wheels-on time and a gate-in time. This is the time that the aircraft spends on the airport surface with engines on, and includes the time spent on the taxiway system and in the runways queues. The wheels-on is the stage when the aircraft touches down the ground. The gate-in is the process of arrival of the aircraft to the gate or the parking position. On arrivals, the runway time is the time the aircraft touches down on the runway. The arrival gate time includes the time the aircraft takes to taxi to the gate. The taxi-in is the unimpeded time to traverse the surface from the runway exit until existing the movement area. Unimpeded taxi-in time is the estimated taxi-in time for the aircraft by carrier under optimal operating conditions.

[032] The taxi-in time of the aircraft is represented by three components as the unimpeded taxi-in time, the time spent in the arrival queue, and the congestion delay due to ramp and taxiway interactions. Furthermore, there are other factors such as runway configuration, the airline/terminal, weather conditions of the terminal, downstream restrictions of the airport, and the arrival queue of the aircraft over the airport.

[033] It would be appreciated that the calculation of moving average is performed for the error value over several window sizes ranging from 2 to 15 as standard. After calculating the K-MA, where K is the window size range, a new prediction is calculated by adding the K-MA values of the error with the original predicted value. Further, it calculates error for the new prediction from the actual value. This process is repeated for all the K-MA for all the flights. Then standard deviation is calculated for all the error values calculated from K-MA along with the standard of the original error.

[034] It is to be noted that a least standard deviation of the K-MA is selected. It would be appreciated that the least standard deviation must be less than the standard deviation of the original error. If both the conditions are met for a particular K-MA, its value is considered as the best prediction for the flight. It should also be appreciated that the moving the prediction slightly closer to the direction of the error will improve the accuracy of the model. Further, the accuracy may be improved by considering then K-value up to N-1, where N is the number of records available for that particular flight. It addition to this, the model can perform a moving average with a backward moving windows to identify the backward trend in the data flow.

[035] The moving average model is used to improve the accuracy of the taxi-in time using machine learning models. The moving average model is performed for the error value over several window sizes. The moving average filters the prediction out for the flight and calculates an error comparing with the actual value. Let k be a point on the standard normal distribution curve that gives the corresponding accuracy as:
cum(normaldist(k)) – cum(normaldist(-k)) = a (1)
where cum(normaldist) refers to cumulative normal distribution. If cum(normaldist(k)) = 1- cum(normaldist(-k)), applying in equation (1), then
1 - cum(normaldist(-k)) – cum(normaldist(-k)) = a; and (2)
cum(normaldist(-k)) = (1-a)/2). Therefore, k = -(normalinverse((1-a)/2)).

It is to be noted that to obtain a normal distribution with m minutes for the accuracy range, the model uses the standard deviation obtained as
SD = m/k.

It would be appreciated that the above mentioned steps are repeated for different values of accuracy to get corresponding SD values. The obtained SD values are used to generate a sample of random numbers which are normally distributed with zero mean. The random noise is then added to the actual EoN value to convert it to a specific accuracy.

[036] A standard deviation is calculated for a normal distribution having various accuracy levels for a specific time interval. Random numbers which are normally distributed with unit mean and the calculated standard deviations are computed as noise. The EoN information is simulated for various accuracy by adding normally distributed noise to the original EoN values.

[037] It would be appreciated that the determination module (112) of the system (100) is also configured to analyze one or more received airline data by utilizing a plurality of models of the system. The plurality of models include a network planning model, one or more statistical model and a delay prediction engine. Here, a set of attributes associated with the set of airlines data is identified. For example, the set of attributes include day of week, seasons, OD (Origin-Destination) pair, tail number and epoch. Further, a probability distribution is calculated for departure delay and arrival delay by utilizing the set of parameters.

[038] In one aspect, the network planning model of the system (100) receives the aircraft network data for example, operating airline, marketing airline (if a code-share leg), origin, destination, flight number, departure and arrival times, equipment, days of operation, leg mileage and flight time, a neighbors’ airport data (for example, Gate, Runway root, distance between each airports), air traffic data and weather data and suggests an alternative path when there is any aircraft delay.
[039] In another aspect, the one or more statistical model receives a plurality of airline historical data (for example, Airport data, weather data, gate delay, taxi-out delay, airborne delay and taxi-in delay) and provides Model to fit real time data and predict delay.

[040] In the preferred embodiment, the simulation module (114) of the system (100) is configured to simulate the received EoN information and the determined taxi-in time of the flight. Further, the simulation is also considering a real time flight information, one or more operational scenarios, and one or more operational levers of the flight.

[041] It should be appreciated that the distributed data is generated for various standard deviations and zero mean based on the allowed intervals of minutes for accuracy. The generated data is then added to the actual value of EoN information to get a simulated EoN information with various accuracy. The simulated data is used to cross check the ETA accuracy based on the predicted taxi-in.

[042] In the preferred embodiment, a prediction module (116) of the system (100) is configured to predict the estimated time of arrival of the flight using simulation results of the EoN information and the determined taxi-in time of the flight.

[043] Referring FIG. 3, a processor-implemented method (200) to predict estimated time of arrival of a flight is provided. The processor-implement method comprising one or more steps to execute the prediction by simulating EoN information and a determined taxi-in time along with historical operations (arrival and departure) data, historical airport data (captured at the time of arrival and departure) including congestion information, weather data, etc. Further, the process categorizes input data related to airline history, airline network, airport data and various airlines reference data. Further, the process analyzes the one or more causes of flight delays which may be due to maintenance issues with the aircraft, fuelling, weather, congestion in air traffic, security issues etc.

[044] Initially, at the step (202), EoN information of the flight is received as an input at a receiving module (106) of the system (100). The ETA prediction using a simulation of computed taxi-in time and the received EoN information of the flight.

[045] In the preferred embodiment, at the next step (204), one or more operational scenarios are analyzed at an operation characterization module (108) of the system (100) considering one or more dimensional aspects and the received input to the system (100). It would be appreciated that the one or more operational scenarios includes fleet deployment pattern, network flow characterization, and operational preferences.

[046] In the preferred embodiment, at the next step (206), the one or more analyzed operational scenarios are learned at a learning module (110) of the system (100). It is to be noted that the learning of operational scenarios defines one or more operational levers, wherein the one or more operational levers include needs of a crew, operations of one or more gates, and a rescheduling priorities.

[047] In the preferred embodiment, at the next step (208), a taxi-in time is determined at a determination module (112) of the system (100) using a moving average model. Wherein the MA model is used to learn the behavior of the taxi-in time of the flight. The taxi-in time is defined as time between a wheels-on time and a gate-in time. The taxi-in time is analyzed with a decision tree model to obtain one or more prediction values. The obtained one or more prediction values are filtered out for an individual flight. It would be appreciated that the taxi-in time of the flight depends on one or more factors such as runway configuration, downstream restrictions, and arrival queue.

[048] The taxi-in time is defined as time between a wheels-on time and a gate-in time, taxi-out time is defined as time between the actual pushback and takeoff of the flight, and the air time of the flight includes total time from the moment that an aircraft first moves under its own power for the purpose of taking off until the moment the aircraft comes to rest at the end of the flight.

[049] In the preferred embodiment of the disclosure, at the step (210), simulating the received EoN information and the determined taxi-in time of the flight at a simulation module of the system. It would be appreciated that the simulation also considers a real time flight information, one or more operational scenarios, and one or more operational levers of the flight.

[050] In the preferred embodiment of the disclosure, at the last step (212), estimated time of arrival of the flight is predicted at a prediction module (116) of the system (100) considering simulated result of the EoN information and the taxi-in time of the flight.

[051] It would be appreciated that the estimated time of arrival prediction of the flight includes a prediction phase, a set of prediction elements, a set of prediction touch points and a set of system of interest. The prediction phase includes a planning phase and an en-route phase. The set of prediction elements include PDC (Passenger Door Closed), CDC (Cargo Door Closed), BRL (Break Released), ASM (Aircraft Start Moving), OUT (Out of Terminal), OFF (Off the ground), ON (On the ground), IN (In the terminal, ASM (Aircraft Stopped Moving), PDO (Passenger Door Open), CDO (Cargo Door Open) and BRS (Break Set). The set of prediction touch points includes a propagate network time, an en-route time, a gate time, a turnaround time. The set of system of interest include an ACARS (Aircraft Communications Addressing, and Reporting System), a FLIFO (Flight Information), a SHARE (Schedule Airlines Reservation System), a SWIM (System Wide Information Management System), a Sabre FPM (Flight Plan Manager), a TAF (Terminal Aerodrome Forecast), an Airport DB (Airport Configurations) and a Self-Park system. The FPM is a tool for developing and comparing routes to obtain the least cost route solution. Further, the FPM can easily amend a route, optimize from station to station, fix to fix, station to fix, and fix to station. Additionally, the FPM is capable of building a route to avoid certain fixes, Flight Information Region (FIR) boundaries, and segments of airways. Additionally, the FPM can also accept a route or portion of a route using the “cut and paste” function. The FPM is also able to display a route or routes in comparison to each other and overlay them on a selected weather chart as well as graphically display the profile of a routing with terrain, restricted areas, and airway restriction features. Further, the Self-park provides automated docking guidance to arriving aircraft, allowing the aircraft to safely park at the gate.

[052] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.

[053] The embodiments of present disclosure herein addresses unresolved problem of estimated time of arrival of the flight in real time. The embodiment, thus provides a system and method to predict ETA of the flight. Moreover, the embodiments herein further provide a system and method to predict ETA of the flight while considering historical operations (arrival and departure) data, historical airport data (captured at the time of arrival and departure) including congestion information, and weather data of the airport. The ETA prediction involves prediction of arrival and departure times of flight. Herein, the method categorizes input data related to an airline history, airline network, airport data and various airline reference data. Further, the method analyses other reasons which may be due to maintenance issues with the aircraft, fuelling, weather, congestion in air traffic, and security issues etc.

[054] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

[055] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[056] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

[057] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[058] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.