Predictive Wait Time Estimation Using Dynamic Transaction Modeling
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Solution Overview
Problem
Traditional queuing theory is inadequate for accurately predicting wait times in service systems due to its assumption of constant service and arrival rates, which does not reflect real-world variations such as time of day and season, and lacks the ability to monitor wait times without direct observation.
Innovation Solution
A system and method using predictive modeling that generates wait time estimates based on transaction data, including service rate, arrival rate, and queue length, allowing for automatic notifications and adjustments to service rates or promotions when thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional queuing theory is used with constant service and arrival rates, then the model is simple and easy to implement, but the prediction accuracy deteriorates because it does not reflect real-world variations
Solution Approach 1:
The patent transforms the static queuing model into a dynamic one by allowing service rates and arrival rates to vary over time. The system divides the time period into multiple intervals and estimates rates for each interval, enabling the model to adapt to changing conditions throughout the day while maintaining computational feasibility through structured data organization and processing.
Solution Approach 2:
The patent changes the parameters of the queuing model from constant values to time-varying parameters. By estimating service rates and arrival rates for different time intervals and incorporating time-of-day effects, the model achieves higher prediction accuracy while managing complexity through systematic parameter estimation from transaction data.
2Measurement precision
If direct observation of customer arrivals and services is implemented, then wait time measurement precision is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent uses transaction data as an intermediary to infer queue metrics without direct observation. Instead of installing sensors or observation systems at the merchant location, the system processes existing transaction records to estimate arrival rates, service rates, and queue lengths, significantly reducing system complexity while maintaining measurement capability.
Solution Approach 2:
The patent replaces the mechanical/physical observation system with an information processing system. By substituting direct customer flow monitoring with computational analysis of transaction data, the system achieves wait time measurement without the complexity of physical monitoring infrastructure.
3Measurement precision
If time-varying service and arrival rates are incorporated into the model, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the time period into multiple intervals and processes each interval separately with its own estimated rates. This segmentation approach allows the model to capture time variations while keeping computational complexity manageable by breaking down the overall calculation into smaller, more tractable sub-problems that can be solved systematically.
Data Source
AI summary
Described are a system, method, and computer program product for generating and applying a predictive wait time estimate using predictive modeling. The method includes receiving initial transaction data representative of a plurality of transactions between a plurality of transaction accounts and at least one merchant. The method also includes generating, for each subinterval, training data including at least one of the following: service rate during the subinterval, number of transactions during the subinterval, total transaction value during the subinterval, mean transaction value during the subinterval, year, month, day of week, or any combination thereof. The method further includes generating a predictive model that generates an output of arrival rate for an input of at least one time parameter. The method further includes generating the predictive wait time estimate for a designated time.