Progressive Reservation System for Real-Time Itinerary Management
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Solution Overview
Problem
Conventional itinerary management systems fail to provide effective real-time adjustments for trip interruptions like flight delays and cancellations, and do not adequately consider user preferences such as seat and airline preferences when offering alternative reservations.
Innovation Solution
A progressive reservation system utilizing machine learning to aggregate and monitor user data, continuously track transit reservations, and automatically identify and recommend suitable alternative reservations based on historical and real-time information, using a graphical user interface to present these options to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional itinerary management techniques are used, then the payment processor maintains a simple passive role, but the system fails to provide real-time monitoring and alternative recommendations for trip interruptions
Solution Approach 1:
The system enables self-service by automatically monitoring trip interruptions and generating alternative recommendations without requiring user intervention. The payment processor proactively detects flight delays or cancellations and presents alternative itineraries to users, transforming the passive role into an automated self-serve system that improves reliability while maintaining operational simplicity.
Solution Approach 2:
The system implements feedback loops by continuously monitoring trip status through real-time data feeds from carriers and travel services. When interruptions are detected, the system automatically generates alternative recommendations and presents them to users, creating a closed-loop feedback mechanism that enhances trip interruption handling reliability without proportionally increasing system complexity.
2Ease of operation
If the payment processor proactively monitors and provides alternative recommendations, then trip interruption handling improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system uses intermediaries such as travel services, carriers, and data feeds to obtain trip information and alternative recommendations. By leveraging these external intermediaries, the payment processor can provide enhanced user experience with proactive monitoring and alternative suggestions without bearing the full complexity of building all monitoring and recommendation capabilities in-house.
Solution Approach 2:
The system performs preliminary actions by pre-fetching and caching trip information, user preferences, and alternative itinerary options before interruptions occur. This allows the system to quickly present recommendations when needed, improving ease of operation during critical moments while distributing computational load over time rather than concentrating it during interruptions.
3Measurement precision
If user preferences such as seat and airline preferences are considered, then recommendation quality improves, but data processing complexity increases
Solution Approach 1:
The system applies local quality by focusing preference matching on specific local aspects of the itinerary such as seat preferences, airline preferences, and time preferences rather than attempting to match all possible attributes. This selective approach improves recommendation quality for user-cared-about dimensions while keeping data processing complexity manageable by not attempting exhaustive matching across all parameters.
Data Source
AI summary
A method for facilitating intelligent itinerary management via a progressive reservation system is disclosed. The method includes aggregating itinerary information for a user from user transactions, the itinerary information corresponding to a transit reservation; continuously monitoring, in real-time via an application programming interface, the transit reservation by using the itinerary information; determining, based on a result of the continuous monitoring, whether parameters that correspond to the transit reservation satisfy a predetermined threshold, the parameters including an operational status; automatically retrieving, from a repository, historical information that corresponds to the user when the parameters satisfy the predetermined threshold; automatically identifying, in real-time using a model, a future transit reservation based on the itinerary information and the historical information; and providing, via a graphical user interface, the identified future transit reservation to the user.