Automated Split Ticketing Using Probability Thresholds
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Solution Overview
Problem
Current travel search tools are limited in providing comprehensive low-fare travel solutions due to the complexity and time-consuming nature of manual split ticketing, which involves multiple flight connections and carriers, overwhelming computing resources, and exponential growth in possible partitions, preventing providers from offering complete multi-ticket solutions.
Innovation Solution
An automated travel search tool with a ticket splitter that evaluates all ticket partitions using a hierarchical tree of split criteria updated by GDS transactions, assigning probability values and selecting partitions with a defined threshold, and a back-end fare search engine to price these solutions, maintaining efficient computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual split ticketing is performed to find lower-fare multi-ticket solutions, then travel cost savings are improved, but time consumption and computing resource usage increase exponentially
Solution Approach 1:
The system pre-computes and stores optimal split-ticket partitions in a database before actual travel searches. When a user searches for flights, the system retrieves pre-calculated partition solutions rather than computing them in real-time, dramatically reducing query response time while maintaining comprehensive coverage of possible ticket combinations
Solution Approach 2:
The system segments the travel journey into multiple independent ticketable segments automatically. Instead of treating the entire journey as a single booking unit, the system identifies valid break points where tickets can be split, evaluating each segment组合 to find optimal cost-saving opportunities without manual intervention
2Reliability
If exhaustive split ticketing combinations are evaluated to ensure complete low-fare solutions, then solution completeness is improved, but computing resource consumption increases exponentially
Solution Approach 1:
The system implements a tiered evaluation approach where it first evaluates the most promising partition combinations based on historical data and pricing patterns, then progressively evaluates additional combinations only if needed. This allows the system to achieve sufficient solution completeness without exhaustively evaluating all possible partitions, thereby controlling computing resource consumption
Solution Approach 2:
The system pre-identifies and stores valid partition structures and pricing rules in a database before actual searches. During query processing, it retrieves and applies these pre-computed structures rather than generating all combinations from scratch, significantly reducing the computational burden while maintaining solution completeness
3Adaptability or versatility
If the number of ticket partitions to evaluate is increased to cover all possible combinations, then coverage of low-fare opportunities is improved, but the complexity of the system increases exponentially
Solution Approach 1:
The system segments the complex partition evaluation problem into manageable components: valid break point identification, segment pricing evaluation, and combination optimization. By breaking down the problem into these discrete steps with clear rules, the system achieves comprehensive coverage without exponential complexity growth
Solution Approach 2:
The system pre-establishes pricing rules, valid partition structures, and evaluation criteria in a database before actual travel searches. This preliminary structuring allows the system to handle diverse ticket combinations systematically through rule-based evaluation rather than complex ad-hoc computations, maintaining manageability while achieving broad coverage
Data Source
AI summary
An enhanced travel search tool aimed at providing travel opportunities includes a ticket splitter operating from a list of flight connections provided by a front-end flight search engine. The ticket splitter comprises a unit for valuating all ticket partitions generated from the provided list of connection flights and a tree of split criteria updated in a cache for determining the partitions and attributing to each partition a probability value of obtaining a split-ticket solution. The ticket splitter also includes a unit for selecting those of the valuated partitions that have a probability value higher than a defined threshold and provides a sorted list of recommended partitions to a back-end fare search engine to price all the selected partitions including a reference single-ticket solution. Travel search tool offering of travel opportunities is enhanced by automatically including multi-ticket travel solutions.