Machine Learning Flight Scheduling System
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Solution Overview
Problem
Booking a flight has become complicated due to numerous unsuitable options presented to travelers, often resulting from lack of available seating that meets their preferences, such as preferred dates and seating environments.
Innovation Solution
A passenger service system utilizing machine learning logic to search flight schedules based on user preferences, trained with passenger data, and updates its training data after bookings to improve future matching, providing suitable flight options and facilitating bookings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If airlines present numerous flight options to travelers, then the variety of choices increases, but the difficulty of booking increases and travelers may not find suitable options
Solution Approach 1:
The system collects passenger feedback data including flight selections, preferences, and booking behaviors. This feedback is continuously fed back into the machine learning model to refine and personalize flight recommendations, improving the matching between passenger needs and available flights over time.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on passenger profiles, historical data, and real-time conditions. By changing the parameters used to filter and rank flights (such as preferred times, airports, airlines, and price ranges), the system presents the most relevant options from the available variety, making booking easier while maintaining adaptability.
2Productivity
If airlines reduce the number of flights to fill more seats, then revenue per flight increases, but the number of available seating options decreases
Solution Approach 1:
The system performs preliminary matching of passengers to suitable flights and seats before the booking process begins. By pre-analyzing passenger preferences and availability, the system identifies optimal flight-seat combinations in advance, ensuring that even with fewer flights, passengers find suitable seating options that meet their needs.
3Productivity
If airlines charge extra fees for ancillary services and particular seats, then revenue increases, but the complexity of booking increases
Solution Approach 1:
The system automatically calculates and presents the total cost including all ancillary services and seat selections based on passenger preferences. The machine learning model predicts which ancillary services a passenger is likely to want and pre-configures appropriate options, allowing passengers to review and confirm rather than manually configure each option, thereby reducing perceived complexity while maintaining revenue opportunities.
Data Source
AI summary
A computer-implemented method includes searching, by machine learning logic of a computer, a flight schedule database for one or more flight schedule records related to flight preference information specified by a user. The machine learning logic is trained with training data that includes flight schedules selected by passengers and flight preference information associated with the passengers. The method further includes responsive to locating the one or more flight schedule records, communicating, by the computer, flight schedules associated with the one or more flight schedule records to a terminal associated with the user. The method includes receiving, from the terminal, a booking indication associated with a particular flight schedule of the flight schedules and updating the training data to associate the particular flight schedule with the flight preference information specified by the user.