KR-20260067108-A - METHOD AND DEVICE FOR PREDICTING TRAFFIC DEMAND ON A MULTI-WAY POINT-BASED THREE-DIMENSIONAL CORRIDOR

KR20260067108AKR 20260067108 AKR20260067108 AKR 20260067108AKR-20260067108-A

Abstract

A method and apparatus for predicting traffic demand on a three-dimensional corridor based on multiple waypoints are disclosed. According to one embodiment of the present invention, a method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints comprises: a step of collecting detailed area information regarding a corridor through which an aircraft passes during a demand prediction time interval; a step of analyzing the detailed area information to identify a three-dimensional area constituting the corridor; a step of calculating an entry time when the aircraft enters the three-dimensional area and an exit time when the aircraft exits the three-dimensional area; and a step of counting traffic demand in the corridor using the entry time and the exit time.

Inventors

정명숙
전대근
김현경
은연주
오은미
김소윤
이소망
김혜욱
장영훈

Assignees

한국항공우주연구원

Dates

Publication Date: 20260512
Application Date: 20241105

Claims (15)

A step of collecting detailed area information regarding the corridor through which the gas passes during the demand forecast time interval; A step of identifying three-dimensional areas constituting the corridor by analyzing the above detailed area information; A step of calculating the entry time when the gas enters the three-dimensional region and the exit time when the gas exits the three-dimensional region; and A step of counting traffic demand in the corridor using the above entry time and the above exit time. A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, including
In paragraph 1, The above corridor is, The above three-dimensional region is configured to include at least one of a prism and a cylinder, and The above calculation step is, A step of calculating each entry time and each exit time of the gas for each of the above-mentioned prisms; and Step of calculating the circle entry and circle exit times of the gas for each of the above cylinders A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, including
In paragraph 2, The above-mentioned counting step is, As a step of conducting demand quantity counting based on occupancy time, A step of dividing the above demand quantity prediction time intervals into defined time intervals; A step of determining the interval between the earliest leading time among each of the above entry times and the above original entry times, and the latest trailing time among each of the above exit times and the above original exit times, as the occupancy time; and A step of predicting traffic demand by allocating the above occupancy time to the above demand prediction time interval and counting the number of time intervals where the above occupancy time overlaps. A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, including
In paragraph 3, For a single three-dimensional region, when the gas exits and then re-enters, and multiple entry and exit times are calculated, The above-mentioned counting step is, As a step of conducting demand quantity counting based on occupancy time, A step of determining a plurality of occupancy times based on the point in time when the gas leaves the above-mentioned three-dimensional region; and A step of predicting traffic demand by allocating each of the plurality of occupancy times to the demand prediction time interval, individually counting the number of time intervals where the plurality of occupancy times overlap, and summing them. A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, further including
In paragraph 2, The above-mentioned counting step is, As a step of conducting demand quantity counting based on entry time, A step of dividing the above demand quantity prediction time intervals into defined time intervals; A step of determining the fastest leading time among each of the above entry times and the above original entry time; and A step of predicting the traffic demand by counting the number of time intervals within the demand prediction time interval that match the above-mentioned lead time. A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, including
In paragraph 1, The above calculation step is, [Equation 4] A step of calculating the time T P1 when the gas passes the intersection point P1 between the predicted trajectory of the gas and the boundary of the three-dimensional region, satisfying the condition; and i) if the above intersection point P1 is generated as the gas enters the above three-dimensional region, T P1 is identified as the entry time, and ii) if the above intersection point P1 is generated as the gas exits the above three-dimensional region, T P1 is identified as the exit time. - The above P S and the above P E are the start and end points of a trajectory link that intersects the highest or lowest altitude of the three-dimensional area among the trajectory links connecting adjacent trajectory points located within a partial segment, and The above T PS is the time when the gas passes through P S , and the above T PE is the time when the gas passes through PE , and The above h1 is the absolute value of the altitude difference between PS and PE , and the above h2 is the absolute value of the altitude difference between PS and P1. A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, including
In paragraph 6, The above traffic demand forecasting method is, [Formula 5] A step of calculating the latitude Lat P1 when the gas passes through the intersection point P1, satisfying the condition; [Equation 6] A step of calculating the longitude Long P1 when the gas passes through the intersection point P1, satisfying the above; and [Equation 7] A step of calculating the altitude H P1 when the gas passes the intersection point P1, satisfying the condition. - The above Lat PS , the above Long PS , and the above H PS are the latitude, longitude, and altitude, respectively, when the gas passes through PS , and The above Lat PE , the above Long PE , and the above H PE are the latitude, longitude, and altitude, respectively, when the gas passes through PE . A method for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, including
An identification unit that collects detailed area information regarding a corridor through which a gas passes during a demand forecast time interval, analyzes the detailed area information, and identifies a three-dimensional area constituting the corridor; A calculation unit that calculates the entry time when the gas enters the three-dimensional region and the exit time when the gas exits the three-dimensional region; and A processing unit that counts the traffic demand in the corridor using the above entry time and the above exit time. A multi-waypoint-based three-dimensional corridor traffic demand forecasting device including
In paragraph 8, The above corridor is, The above three-dimensional region is configured to include at least one of a prism and a cylinder, and The above calculation unit is, Calculate the respective entry and exit times of the gas for each of the above prisms, and Calculating the circle entry and circle exit times of the gas for each of the above cylinders, Multi-waypoint based 3D corridor traffic demand prediction device.
In Paragraph 9, The above processing unit is, As a measure of demand quantity counting based on occupancy time, The above demand forecast time intervals are divided into defined time intervals, and The time between the earliest leading time among the above-mentioned entry times and the above-mentioned original entry times, and the latest trailing time among the above-mentioned exit times and the above-mentioned original exit times, is determined as the occupancy time. Predicting the traffic demand by allocating the above occupancy time to the above demand prediction time interval and counting the number of time intervals where the above occupancy time overlaps, Multi-waypoint based 3D corridor traffic demand prediction device.
In Paragraph 10, For a single three-dimensional region, when the gas exits and then re-enters, and multiple entry and exit times are calculated, The above processing unit is, As a measure of demand quantity counting based on occupancy time, A plurality of occupancy times are determined based on the point in time when the gas leaves the above-mentioned three-dimensional region, and By allocating each of the plurality of occupancy times to the demand prediction time interval, and individually counting and summing the number of time intervals where the plurality of occupancy times overlap, the traffic demand is predicted. Multi-waypoint based 3D corridor traffic demand prediction device.
In Paragraph 9, The above processing unit is, As a result of implementing demand quantity counting based on entry time, The above demand forecast time intervals are divided into defined time intervals, and Determine the fastest leading time among the above-mentioned entry times and the above-mentioned original entry times, and Predicting the traffic demand by counting the number of time intervals within the demand prediction time interval that match the above-mentioned lead time, Multi-waypoint based 3D corridor traffic demand prediction device.
In paragraph 8, The above calculation unit is, [Equation 4] Satisfying this, calculate the time T P1 when the gas passes the intersection point P1 between the predicted trajectory of the gas and the boundary of the three-dimensional region, and i) If the above intersection point P1 is generated as the gas enters the above three-dimensional region, T P1 is identified as the entry time, and ii) If the above intersection point P1 is generated as the gas exits the above three-dimensional region, T P1 is identified as the exit time. - The above P S and the above P E are the start and end points of a trajectory link that intersects the highest or lowest altitude of the three-dimensional area among the trajectory links connecting adjacent trajectory points located within a partial segment, and The above T PS is the time when the gas passes through P S , and the above T PE is the time when the gas passes through PE , and The above h1 is the absolute value of the altitude difference between PS and PE , and the above h2 is the absolute value of the altitude difference between PS and P1. Multi-waypoint based 3D corridor traffic demand prediction device.
In Paragraph 13, The above calculation unit is, [Formula 5] Satisfying this, calculate the latitude Lat P1 when the gas passes through the above intersection point P1, and [Equation 6] Satisfying this, the longitude Long P1 is calculated when the gas passes through the above intersection point P1, and [Equation 7] Satisfying, calculating the altitude H P1 when the gas passes the intersection point P1, - The above Lat PS , the above Long PS , and the above H PS are the latitude, longitude, and altitude, respectively, when the gas passes through PS , and The above Lat PE , the above Long PE , and the above H PE are the latitude, longitude, and altitude, respectively, when the gas passes through PE . A multi-waypoint-based three-dimensional corridor traffic demand forecasting device including
A computer-readable recording medium having a program for executing any one of the methods of paragraphs 1 through 7.

Description

Method and Device for Predicting Traffic Demand on a Multi-Waypoint-Based Three-Dimensional Corridor The present invention provides a method and apparatus for predicting traffic demand on a three-dimensional corridor based on multiple waypoints, which estimates traffic demand by considering the entry time, exit time, and occupancy time of a gas for a corridor as a three-dimensional area composed of a combination of prisms and cylinders. In this embodiment, air traffic may be used as a demand company/institution, and the air traffic sector may be used as a market and application field. Registration No. 10-2681447 (2024.07.01) "Real-time lane queue prediction system using image detector, method, and recording medium storing a computer-readable program for executing the method" Registration No. 10-2681447 discloses a system and method that performs real-time lane-by-lane queue prediction and effectively provides real-time lane-by-lane queue prediction information, thereby enabling more accurate prediction of vehicle queues even with low installation and maintenance costs. Air traffic demand refers to the number of aircraft operating in airports and airspace. Demand forecasting refers to predicting the number of aircraft to operate at an airport and in a specific airspace during a given period in the future. Demand forecasting methods studied to date are broadly divided into deterministic forecasting methods that do not consider uncertainty and probabilistic forecasting methods that probabilistically consider uncertainty. The deterministic prediction method is a method for counting the number of aircraft to operate in a specific airport and airspace based on a predicted 4D flight trajectory (aircraft position and time information) under the assumption that the aircraft departs exactly at the expected departure time on the flight plan and flies without delay along a pre-planned flight path. The deterministic forecasting method has the advantage of being able to forecast demand using only flight plans and not requiring complex calculations. In contrast, the probabilistic forecasting method is a method that predicts demand by statistically modeling uncertainties such as aircraft departure times, flight delays, and changes in flight paths, and reflecting them. Probabilistic forecasting methods have the advantage of being able to provide reliability information regarding the predicted demand quantity in addition to the predicted demand value, as they forecast demand by considering probabilistic characteristics. However, probabilistic prediction methods require the development of uncertainty models for individual airports and specific airspaces, and have the disadvantage of being computationally complex compared to deterministic prediction methods. Traditional air traffic demand forecasting focused on manned aircraft for airport and airspace demand; however, with the recent emergence of various aircraft types—such as unmanned aerial vehicles, drones, and Urban Air Mobility (UAM)—it is now necessary to manage traffic flow and forecast demand for them as well. Furthermore, there is a lack of technology to predict air traffic demand in three-dimensional areas where unmanned aircraft fly. Therefore, there is an urgent need for improved technology to predict air traffic demand for unmanned aircraft flying in corridors as three-dimensional domains. FIG. 1 is a block diagram illustrating the configuration of a traffic demand prediction device on a three-dimensional corridor based on multiple waypoints according to an embodiment of the present invention. FIG. 2 is a flowchart for predicting traffic demand in a corridor according to the present invention. Figure 3 is a diagram illustrating the intersection between the trajectory link and the sector boundary link. FIG. 4 is a diagram illustrating a segment corresponding to the section through which a gas defined in a two-dimensional plane passes through a target sector. Figure 5 is a figure illustrating an example of identifying partial segments. FIG. 6 is a diagram illustrating a method for calculating the sector entry point/sector exit point and sector entry time/sector exit time of a partial segment. Figure 7 is a figure for explaining the definition of a circle area. Figure 8 is a flowchart for calculating the Entry time and Exit time for a Target circle. Figure 9 is a figure illustrating a schematic filter area for a target circle. Figure 10 is a figure illustrating the identification of a track segment for a target circle. Figure 11 is a figure for explaining the identification of a partial segment of a target circle. Figure 12 is a diagram illustrating the entry and exit times of a gas into a corridor. Figure 13 is a figure for explaining demand counting based on occupancy time. Figure 14 is a figure for explaining demand counting based on entry time. FIG. 15 is a flowchart illustrating a method for predicting traffic demand on a three-dimensional corridor based on mult