Wait Time Prediction Model Using Transaction Data Clustering
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Solution Overview
Problem
Consumers face difficulties in determining accurate wait times at resource providers due to varying predictions and lack of real-time information, making it hard to make informed choices.
Innovation Solution
A system that predicts wait times by analyzing transaction data using clustering techniques to identify user segments and local resource providers, estimating arrival and service rates through Poisson regression, and calculating queue times based on these probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If resource providers post their own wait time predictions, then resource providers can provide wait time information to consumers, but the predictions may be biased and consumers cannot determine honesty
Solution Approach 1:
The patent introduces an intermediary system (wait time prediction system) that collects data from multiple resource providers and processes it through clustering algorithms and probability distributions. This intermediary layer filters out individual provider biases by aggregating data across the network, using user segment behavior patterns to generate more objective wait time estimates that consumers can trust.
Solution Approach 2:
The system merges wait time data from multiple resource providers into a comprehensive prediction model. By combining transactions data, user segment information, and probability distributions across the network, the system creates a unified view of wait times that is more reliable than any single provider's self-reported data.
2Loss of information
If comprehensive real-time wait time prediction is implemented across multiple resource providers, then consumers can make informed choices, but system complexity increases
Solution Approach 1:
The system segments users into distinct user segments based on their transaction patterns and behavior. By dividing the user base into segments and analyzing each segment's probability distribution separately, the system manages complexity through modular processing rather than treating all users uniformly. This segmentation allows the complex prediction model to be broken down into manageable segments that can be processed independently.
Solution Approach 2:
The system changes parameters dynamically by using probability distributions that are updated in real-time based on incoming transactions data. Rather than using fixed wait time estimates, the system adjusts probability parameters (arrival rates, service rates) continuously, allowing the model to adapt to changing conditions without requiring complete system reconfiguration.
3Measurement precision
If user segmentation and probability distribution analysis are performed in real-time, then accurate wait time predictions are achieved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-segmenting users into groups based on their transaction patterns and pre-calculating probability distributions for each segment. This preliminary processing allows the system to quickly retrieve and combine pre-computed data when generating real-time wait time predictions, avoiding the need to perform complex segmentation and probability calculations from scratch for each prediction request.
Data Source
AI summary
Described herein is a system and method for estimating wait times at various resource providers based on transaction information submitted to a processing network. In some embodiments, the system identifies a time distance between a target resource provider and a plurality of other resource providers in order to identify resource providers local to the target resource provider. Additionally, the system may identify segments for each of a plurality of users on the system. The processing network may identify recent transaction requests received from each of the resource providers local to the target resource provider. The processing network may then identify users associated with each of those transaction requests. Based on the segments assigned to each of those users, the system may estimate and arrival rate, and subsequently an estimated wait time, for the target resource provider.