The boeing company (20240311728). AIRCRAFT CONGESTION REDUCTION AT AIRPORT simplified abstract
Contents
AIRCRAFT CONGESTION REDUCTION AT AIRPORT
Organization Name
Inventor(s)
Umesh Kallappa Hosamani of Bangalore (IN)
Akshay Arun Sankeshwari of Bengaluru (IN)
Ajaya Srikanta Bharadwaja of Bangalore (IN)
Veeresh Kumar Masaru Narasimhulu of Bangalore (IN)
AIRCRAFT CONGESTION REDUCTION AT AIRPORT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311728 titled 'AIRCRAFT CONGESTION REDUCTION AT AIRPORT
The patent application describes a system for reducing aircraft congestion on the ground at an airport using machine learning technology.
- The system includes a database that stores data collected over at least a year at the airport.
- A computer is used to train a machine learning model with the collected data and generate estimated taxi times for aircraft based on current data.
- A transmitter transfers the estimated taxi times to the aircraft and a control center.
Potential Applications: - This technology can be used to optimize aircraft movement on the ground, reducing congestion and improving efficiency at airports. - Airlines and airport authorities can utilize this system to streamline operations and enhance overall passenger experience.
Problems Solved: - Reduces aircraft congestion on the ground, leading to smoother operations and decreased delays. - Provides accurate estimated taxi times for aircraft, improving overall efficiency and reducing fuel consumption.
Benefits: - Increased operational efficiency at airports. - Reduced fuel consumption and emissions. - Enhanced passenger experience with fewer delays.
Commercial Applications: Title: "Optimizing Aircraft Movement System for Airports" This technology can be commercialized by offering it to airports, airlines, and aviation authorities to improve ground operations and enhance overall efficiency.
Prior Art: Further research can be conducted in the field of machine learning applications in aviation and airport operations to identify any existing technologies or systems similar to the one described in the patent application.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms and their applications in the aviation industry to enhance the efficiency and effectiveness of the system described in the patent application.
Questions about Aircraft Congestion Reduction System: 1. How does the system collect and store data to train the machine learning model? - The system utilizes a database to store data collected over a year at the airport, which is then used to train the machine learning model for estimating taxi times. 2. What are the potential challenges in implementing this system at different airports with varying infrastructure? - The system may face challenges in adapting to different airport layouts and operational procedures, requiring customization and integration efforts.
Original Abstract Submitted
a system for aircraft congestion reduction on a ground at an airport includes a database, a computer, and a transmitter. the database is operational to store collected data gathered over at least a year at the airport. the computer is in communication with the database and is operational to train a machine learning model using the collected data, receive input data approximate a current time, and generate at the current time, based on the input data, the machine learning model, and a plurality of current aircraft, an estimated taxi time for a particular aircraft to move between an assigned gate and an assigned runway via an assigned route along the taxiways. the transmitter is in communication with the computer and is operational to transfer the estimated taxi time to the particular aircraft and a control center.
