Predicting Aircraft Taxi-Out Times

• US 20100185426A1
• Filed: 01/15/2010
• Published: 07/22/2010
• Est. Priority Date: 01/16/2009
• Status: Abandoned Application

• Current Assignee: Lance Sherry, Poornima Balakrishna, Rajesh Ganesan
• Original Assignee: Lance Sherry, Poornima Balakrishna, Rajesh Ganesan
• Inventors: Ganesan, Rajesh, Sherry, Lance, Balakrishna, Poornima
• Application Number:
  US12/688,131
• Publication Number:
  US 20100185426A1
• Time in Patent Office: Days
• US Class Current: 703/6
• CPC Class Codes: G08G 5/065 Navigation or guidance aids, e.g. for taxiing or rolling

Abstract

A taxi-out time predictor includes an airport simulation processing module, a state vector creation processing module, an actual taxi-out value input processing module and a learning processing module. The airport simulation processing module models airport taxi-out dynamics for a predetermined time period. The actual taxi-out value input processing module collects actual taxi-out measurements from departure aircrafts. The learning processing module includes a reinforcement learning estimation processing module, an update utility processing module and a reward processing module. The reinforcement learning estimation processing module generates a predicted taxi-out time value using the variables in the state vector and an output utility value. The aircraft taxi-out time predictor operates iteratively to predict the taxi-out time.

Claims

1. An aircraft taxi-out time predictor comprising:

(a) an airport simulation processing module configured to model airport taxi-out dynamics for a first predetermined time period;

(b) a state vector creation processing module configured to generate a state vector, said state vector including at least the following;

(1) a first state variable configured to represent the average amount of time previous departure aircrafts spent in a runway queue;

(2) a second state variable configured to represent the number of co-taxiing departure aircrafts;

(3) a third state variable configured to represent the number of co-taxiing arrival aircrafts;

(4) a fourth state variable configured to represent an average taxi-out time during a second predetermined time period before a taxi-out time prediction is made; and

(5) a fifth state variable configured to represent the current time;

(c) an actual taxi-out value input processing module configured to collect actual taxi-out measurements from physical departure aircrafts; and

(d) a learning processing module, said learning processing module including;

(1) a reinforcement learning estimation processing module configured to generate a predicted taxi-out time value using;

(i) said state vector; and

(ii) an output utility value;

(2) an update utility processing module configured to calculate said output utility value using a reward value; and

(3) a reward processing module configured to calculate said reward value using;

(i) said actual taxi-out measurements; and

(ii) said predicted taxi-out time value.

2. The aircraft taxi-out time predictor according to claim 1, wherein said aircraft taxi-out time predictor operates iteratively.

3. The aircraft taxi-out time predictor according to claim 1, wherein said predicted taxi-out time value is transferred to a predicted taxi-out time matrix.

4. The aircraft taxi-out time predictor according to claim 1, wherein said predicted taxi-out time value is generated before said aircraft pushes away from an airport gate.

5. The aircraft taxi-out time predictor according to claim 1, wherein said state vector variables are discretized.

6. The aircraft taxi-out time predictor according to claim 5, wherein the discretization for said state vector variables is determined by observing said actual taxi-out time values at an airport during a third predetermined period of time before said aircraft pushes away from an airport gate.

7. The aircraft taxi-out time predictor according to claim 1, wherein said aircraft taxi-out time predictor predicts said predicted taxi-out time value using a Markov decision process.

8. The aircraft taxi-out time predictor according to claim 1, wherein said reinforcement learning estimation processing module uses a Bellman'"'"'s optimality equation.

9. The aircraft taxi-out time predictor according to claim 1, wherein said update utility processing module uses a Robbin-Monro'"'"'s stochastic approximation scheme.

10. The aircraft taxi-out time predictor according to claim 1, wherein said predicted taxi-out time value is averaged in 15 minutes intervals.

11. The aircraft taxi-out time predictor according to claim 1, wherein said predicted taxi-out time value and said actual taxi-out time values includes:

(a) a ramp period;

(b) a taxi period; and

(c) a runway period.

12. The aircraft taxi-out time predictor according to claim 1, wherein said reinforcement learning estimation processing module configured to predict said predicted taxi-out time value that minimizes said output utility value is calculated by said update utility processing module.

13. The aircraft taxi-out time predictor according to claim 1, wherein the current time includes at least one of the following:

(a) the time of day;

(b) the day of the week,

(c) the day in a year;

or

(d) a combination of the above.

14. A process for predicting an aircraft taxi-out time comprising:

(a) modeling airport taxi-out dynamics, by an airport simulation processing module, for a first predetermined time period;

(b) creating a state vector, using a state vector creation processing module, said state vector including at least the following;

(1) a first state variable configured to represent the average amount of time previous departure aircrafts spent in a runway queue;

(2) a second state variable configured to represent the number of co-taxiing departure aircrafts;

(3) a third state variable configured to represent the number of co-taxiing arrival aircrafts;

(4) a fourth state variable configured to represent an average taxi-out time during a second predetermined time period before a taxi-out time prediction is made; and

(5) a fifth state variable configured to represent the current time;

(c) collecting actual taxi-out measurements from physical departure aircrafts using an actual taxi-out value input processing module;

(d) generating a predicted taxi-out time value, by a reinforcement learning estimation processing module, using;

(1) said state vector; and

(2) an output utility value;

(e) calculating said output utility value, by an update utility processing module, using a reward value; and

(f) determining said reward value, by a reward processing module, using;

(1) said actual taxi-out measurements; and

(2) said predicted taxi-out time value.

15. The process according to claim 14, wherein said aircraft taxi-out time predictor operates iteratively.

16. The process according to claim 14, wherein said predicted taxi-out time value is transferred to a predicted taxi-out time matrix.

17. The process according to claim 14, wherein said predicted taxi-out time value is generated before said aircraft pushes away from an airport gate.

18. The process according to claim 14, wherein said state vector variables are discretized.

19. The process according to claim 18, wherein the discretization for said state vector variables is determined by observing said actual taxi-out time values at an airport during a third predetermined period of time before said aircraft pushes away from an airport gate.

20. The process according to claim 14, wherein said aircraft taxi-out time predictor predicts said predicted taxi-out time value using a Markov decision process.

21. The process according to claim 14, wherein said reinforcement learning estimation processing module uses a Bellman'"'"'s optimality equation.

22. The process according to claim 14, wherein said update utility processing module uses a Robbin-Monro'"'"'s stochastic approximation scheme.

23. The process according to claim 14, wherein said predicted taxi-out time value and said actual taxi-out time values includes:

(a) a ramp period;

(b) a taxi period; and

(c) a runway period.

24. The process according to claim 14, wherein said reinforcement learning estimation processing module is configured to predict said predicted taxi-out time value that minimizes said output utility value calculated by said update utility processing module.