Temporary Event Query Clustering for Rideshare Location Search
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Solution Overview
Problem
Rideshare applications struggle to accurately locate and provide transportation to temporary events like pop-up markets or festivals due to their lack of formal addresses and inconsistent naming conventions, leading to inefficient manual address entry or imprecise map-based selection.
Innovation Solution
A dynamic clustering approach that leverages collective user behavior patterns to identify temporary events by analyzing spatial and semantic clustering, generating temporary location entries based on user queries, using a generative neural network to create contextually relevant event names.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional location databases are used for permanent locations, then database structure is simple and established, but temporary events without formal addresses cannot be located
Solution Approach 1:
The system dynamically generates temporary location entries in the database based on real-time user query patterns. When multiple users search for similar terms in proximity to the same geographic area, the system automatically creates a temporary location entry, allowing the database structure to adapt flexibly to temporary events without requiring pre-defined formal addresses.
Solution Approach 2:
The system enables self-service by automatically detecting temporary events through collective user behavior patterns and generating location entries without manual intervention. The database updates itself based on aggregated search data, eliminating the need for manual database maintenance for temporary events.
2Ease of operation
If manual address entry is required for temporary events, then user control is high, but user experience deteriorates and efficiency decreases
Solution Approach 1:
The system performs automatic event detection and location identification based on aggregated user search patterns, eliminating the need for users to manually enter addresses. The system serves itself by generating temporary location entries from collective user behavior data.
Solution Approach 2:
The system performs preliminary analysis of user query patterns to identify temporary events before users need to search for them. By pre-processing search data and detecting event patterns, the system prepares location entries in advance, so users receive ready-made search results without manual input.
3Measurement precision
If map-based selection is used for temporary events, then visual interface is provided, but location precision is insufficient
Solution Approach 1:
The system uses feedback from multiple user queries to refine and confirm temporary event locations. When aggregated search patterns indicate a temporary event, the system validates the location through continued monitoring of user behavior, improving location precision through iterative feedback loops.
Solution Approach 2:
The system automatically identifies and validates temporary event locations through analysis of collective user search patterns, eliminating the need for manual map-based selection. The location identification process is self-service, using aggregated data to precisely pinpoint event locations without user intervention.
4Reliability
If temporary events are not recognized in database, then database maintains consistency, but search functionality is impaired
Solution Approach 1:
The system dynamically adjusts database content by automatically adding temporary location entries when user query patterns indicate temporary events. This dynamic approach maintains database reliability for search functionality while adapting to new event types without requiring complex manual recognition processes.
Data Source
AI summary
Systems and methods herein describe a temporary event generation system. The temporary event generation system accesses a user query, queries a database using the user query, based on a determination that the target location associated with the user query does not exist in the database, the temporary event generation system receives a modified user query, generates a first subset of historical user query data based on filtering the historical user query data using map coordinates, generates a second subset of historical user query data from the first subset of historical user query data based on filtering the first subset of historical user query data using a similarity metric of query terms, and generates a temporary event with event map coordinates and an event location name.