Wait Time Prediction Model Using Historical and Feedback Data
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Solution Overview
Problem
Existing systems do not provide accurate wait time information for businesses and public service establishments, limiting users' ability to make informed decisions about visiting times.
Innovation Solution
A model is generated using historical wait time data, place attributes, and historical temporal signals to predict expected wait times, which can be refined with user feedback, and displayed alongside other relevant business information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If wait time information is collected and published for businesses, then users can make informed decisions about visiting times, but most establishments do not collect or publish this information
Solution Approach 1:
The system enables businesses to automatically publish their own wait time information without manual intervention. By collecting historical wait time data from the business's own operations and automatically generating prediction models, the system eliminates the need for manual data entry and publication, making the information available to users while minimizing the operational burden on the business.
Solution Approach 2:
The patent introduces an intermediary system that collects wait time data from businesses, processes it through machine learning models, and publishes the information to users. This intermediary layer handles the complexity of data collection, model generation, and information dissemination, allowing businesses to focus on their core operations while users receive accurate wait time predictions.
2Measurement precision
If only the number of people at establishments is measured, then real-time busyness can be indicated, but accurate wait time predictions cannot be provided
Solution Approach 1:
The system collects and stores historical wait time data in advance before it is needed for predictions. By accumulating wait time information, place attributes, and temporal signals over time, the system builds a comprehensive dataset that enables accurate wait time predictions when users query the system, transforming raw data into predictive insights.
Solution Approach 2:
The system uses historical wait time data as feedback to continuously improve its prediction models. By analyzing past wait times, place attributes, and temporal patterns, the machine learning models learn from historical performance and adjust their predictions to provide more accurate wait time estimates, transforming raw measurements into predictive knowledge.
3Reliability
If historical wait time data is used to generate predictions, then accurate wait time information can be provided, but the model needs continuous refreshing with new data
Solution Approach 1:
The system implements continuous model training and refreshing by automatically incorporating new wait time data as it becomes available. Instead of periodic manual updates, the machine learning models continuously learn from incoming data streams, ensuring predictions remain accurate and current without requiring manual intervention, thus maintaining reliability while minimizing maintenance overhead.
Data Source
AI summary
The wait time prediction technology determines expected wait times for businesses or other public services using a model generated based on at least historical wait times for the business. In response to a request from a user, an expected wait time for service at the business for at least one particular time period on a particular day of a week is determined using the model and provided for display. User feedback regarding the expected wait time may be requested, and used to refresh the model as new wait times and other information are collected.