Parking Occupancy Signals for Point-of-Interest Wait Time Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Patrons of restaurants and other points of interest often face uncertainty about wait times without reservations, leading to potential loss of customers due to long wait times.
Innovation Solution
A system and method that utilizes current and historic occupancy signals from vehicles, parking establishments, and points of interest to predict wait times, communicated through user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If no reservation system is used, then the point of interest can operate with simpler procedures, but wait times become unmanageable and customers are lost
Solution Approach 1:
The system performs preliminary actions by collecting occupancy data in advance and using machine learning models to predict future wait times before customers arrive. This allows the point of interest to prepare for expected crowd levels without requiring advance reservations, maintaining operational simplicity while managing wait times proactively.
Solution Approach 2:
The system implements continuous feedback loops where occupancy signals from sensors are constantly monitored, analyzed by machine learning models, and used to update predictions. This feedback mechanism enables real-time adjustments to operational strategies based on actual crowd patterns, balancing simplicity with effective wait time management.
2Device complexity
If wait time information is not provided to customers, then the system remains simple, but customers cannot make informed decisions and may leave due to long waits
Solution Approach 1:
The system introduces an intermediary layer consisting of machine learning models and prediction algorithms that process raw occupancy data and transform it into useful wait time estimates for customers. This intermediary handles the complexity of data analysis while presenting simplified, actionable information to end users, maintaining system simplicity from the customer perspective.
Solution Approach 2:
The patent replaces manual wait time estimation methods with automated machine learning-based prediction systems. This substitution handles the computational complexity internally while providing customers with accurate wait time information through simple digital interfaces, reducing perceived system complexity while improving information quality.
3Measurement precision
If occupancy monitoring is implemented, then wait time predictions become accurate, but the system requires additional sensors and data infrastructure
Solution Approach 1:
The system employs multi-functional occupancy sensors that serve multiple purposes: tracking current occupancy levels, detecting customer flow patterns, and providing data for machine learning training. This universal approach maximizes the utility of each sensor deployed, improving measurement precision while minimizing the overall infrastructure required compared to specialized dedicated sensors for each function.
Data Source
AI summary
A method of determining a wait time at a point of interest includes obtaining a current occupancy signal of a parking establishment associated with one or more points of interest, obtaining a historic occupancy signal of the parking establishment, determining, with one or more processors, a predicted wait time for the one or more points of interest associated with the parking establishment based on the current occupancy signal and the historic occupancy signal, and communicating the predicted wait time to a user with a user interface device.