Crowdsourced Wait Time Prediction for Navigation Systems
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Solution Overview
Problem
Conventional navigation systems fail to accurately estimate wait times at points-of-interest, limiting their effectiveness in overall time management as they do not differentiate between varying wait times at different locations, such as hospitals or grocery stores, and rely on manual and unreliable methods for users to obtain wait time information.
Innovation Solution
A computer-implemented method using crowdsourced wait time data from a server that processes measured wait times from various points-of-interest to provide predicted wait times, incorporating data from multiple sources through a wait time server and client system, allowing users to optimize their planning based on both travel and wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional predictive models are used for navigation, then travel time estimates can be provided, but wait time estimates at points-of-interest are not included, leading to inaccurate overall time estimates
Solution Approach 1:
The patent combines travel time estimation and wait time estimation into a unified predictive model. The system integrates multiple data sources including historical wait time data, real-time queue data, and travel time data to provide comprehensive overall time estimates that include both transportation and service waiting periods.
Solution Approach 2:
The patent introduces an intermediary component that specifically handles wait time data collection and processing. This intermediary layer gathers wait time information from various points-of-interest, processes it through predictive models, and integrates it with travel time data to provide accurate overall time estimates.
2Reliability
If manual methods are used to obtain wait time information, then some wait time data can be obtained, but the process is tedious and increases planning time
Solution Approach 1:
The system implements self-service by automatically collecting, processing, and providing wait time information without requiring user intervention. The predictive models automatically gather data from multiple sources, process it through algorithms, and deliver integrated time estimates, eliminating the need for users to manually call or query various locations.
Solution Approach 2:
The system incorporates feedback mechanisms where real-time wait time data from users and systems is continuously collected and fed back into the predictive models. This feedback loop allows the system to update and refine its predictions dynamically, providing accurate and current wait time information automatically.
3Loss of information
If manual techniques are used to query wait times, then some information can be obtained, but the information is not necessarily reliable or accurate and requires frequent updates
Solution Approach 1:
The patent creates a universal system that handles multiple data collection functions through a single integrated platform. The system can collect wait time data from various sources including user submissions, direct system integrations with points-of-interest, and historical databases, processing all this information through unified predictive models to provide reliable and accurate estimates.
Data Source
AI summary
In one embodiment of the present invention, a wait time client enables prediction of wait times (e.g., time required to checkout at a grocery store) based on crowdsourced wait data. In operation, the wait time application downloads predicted wait data from a server. The predicted wait data reflects measured wait times for one or more location, such as the ticket line at a movie theater. The wait time client then selects a wait time data point that corresponds to a location of a point-of-interest. Based on the selected wait time data point, the wait time client determines a predicted wait time at the point-of-interest. Advantageously, by leveraging crowdsourced, deterministically measured wait times, the wait time clients enables the incorporation of realistic and up-to-date predicted wait times into the trip planning process.