Navigation Destination Refinement via Crowd-Sourced User Attribute Filtering
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Solution Overview
Problem
Conventional navigation systems often direct travelers to impractical or inaccessible locations, such as official addresses of destinations that are not drivable or to main entrances of venues without considering alternative locations like car rental facilities or parking garages.
Innovation Solution
A computer-implemented method and system that uses crowd-sourced information to identify a subset of users with similar attributes and situational criteria to determine a more optimized travel destination, allowing rerouting to a different location within a predetermined vicinity, such as a car rental return facility or alternative parking spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional navigation systems direct travelers to official addresses or main entrances, then the navigation system is simple to operate, but the destination may be impractical or inaccessible
Solution Approach 1:
The system collects and analyzes crowd-sourced feedback from multiple users about their actual travel destinations and routes. This feedback loop allows the system to learn from real-world travel patterns and improve destination recommendations, resolving the contradiction between simple operation and reliable, practical destination suggestions.
Solution Approach 2:
The system introduces an intermediary processing layer that analyzes crowd-sourced data from multiple users and generates optimized destination recommendations. This intermediary layer bridges the gap between simple user input and complex destination selection, providing reliable practical destinations without complicating the user interface.
2Measurement precision
If the navigation system uses crowd-sourced data to refine destinations, then the accuracy and practicality of navigation improves, but the system complexity increases
Solution Approach 1:
The system extracts only the essential crowd-sourced data elements needed for destination refinement (user attributes, travel patterns, destination preferences) while filtering out unnecessary information. This extraction approach maintains high accuracy in destination recommendations while managing system complexity by focusing on critical data points.
Solution Approach 2:
The system dynamically adjusts parameters such as the number of users to analyze, the depth of attribute comparison, and the level of destination refinement based on contextual factors. This parameter adjustment allows the system to maintain high accuracy when needed while reducing complexity for routine queries, balancing precision and computational burden.
3Loss of information
If the system analyzes multiple user attributes to identify similar users, then the relevance of crowd-sourced data improves, but the data processing time increases
Solution Approach 1:
The system segments user attributes into hierarchical categories (demographic, behavioral, contextual) and processes them in stages. This segmentation allows the system to quickly filter users based on primary attributes and then apply more detailed attribute matching only to relevant subsets, maintaining information quality while reducing overall processing time.
Solution Approach 2:
The system performs preliminary processing and indexing of user attribute data during off-peak periods or in advance, creating pre-computed user profiles and similarity metrics. This preliminary action reduces the computational burden during real-time query processing, maintaining high information quality while minimizing processing delays for users.
Data Source
AI summary
Technical solutions are described to for refining a travel route based on crowd sourcing. An example computer-implemented method includes receiving a first location as a travel destination of a first user. The method also includes identifying a set of users that indicated the first location as a travel destination. The method also includes determining a first subset of users from the set of users by comparing a first set of attributes associated with the first user and each user from the set of users. The method also includes determining a second subset of users from the first subset of users by comparing a second set of attributes associated with the first user and each user from the first subset of users. The method also includes identifying a second location to which the users from the second subset diverged to when traveling to the first location. The method also includes in response, selecting the second location as the travel destination of the first user.