Trip Route Optimization Using Viewpoint Relevance Scoring
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Solution Overview
Problem
Current vehicle navigation systems fail to customize routes based on individual user preferences for leisure travel, requiring users to rely on external sources for points of interest and manually input location information, which wastes computing resources.
Innovation Solution
A trip planning system that determines an optimized path by receiving user preferences, calculating viewpoint relevance scores, and selecting paths based on these scores and driving times, thereby providing a customized route without the need for external information or manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the navigation system provides basic route determination based on shortest travel time, then the routing function is simple and fast, but it cannot customize routes based on user preferences for leisure travel
Solution Approach 1:
The system segments the routing problem into multiple independent components: basic route determination, points of interest identification, user preference matching, and route optimization. Each component processes specific data independently before integration, allowing the system to handle customization without overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by pre-identifying points of interest along potential routes and pre-calculating their relevance scores based on user preferences before final route determination. This allows the routing algorithm to work with pre-processed data, reducing real-time computational complexity while maintaining customization capability.
2Ease of operation
If users manually input location information for points of interest from external sources, then the navigation can include customized locations, but computing resources are wasted on data entry and processing
Solution Approach 1:
The system performs self-service by automatically identifying and selecting points of interest based on user preferences without requiring manual input. The navigation system queries databases for relevant locations, calculates their relevance to user preferences, and integrates them into the route automatically, eliminating the need for user data entry and reducing computational overhead.
Solution Approach 2:
The system introduces an intermediary layer between the user and the points of interest database. This intermediary automatically matches user preferences with relevant locations, filters results based on route feasibility, and presents optimized options to the user, reducing both user effort and system computational burden.
3Manufacturing precision
If the system calculates multiple paths based on user preferences and points of interest, then the route optimization is improved, but the computational time and resources increase
Solution Approach 1:
The system applies partial action by calculating route optimizations for only the most relevant points of interest rather than all possible locations. It uses filtering mechanisms to identify top candidate points of interest based on user preferences and route proximity, then optimizes paths for these selected points only, achieving satisfactory optimization accuracy with reduced computational effort.
Solution Approach 2:
The system changes parameters dynamically during route calculation by adjusting the number of points of interest to evaluate, the relevance score thresholds, and the optimization depth based on user preferences and computational constraints. This allows the system to balance optimization accuracy with computational time requirements.
Data Source
AI summary
A trip planning system may receive, from a user device, trip data identifying a starting point and a destination point for a trip and feature data identifying weights for trip features. The trip planning system may determine a geographical region of interest based on the starting point and the destination point. The trip planning system may receive viewpoint data identifying viewpoints located within the geographical region and may calculate, based on the feature data, viewpoint relevance scores for the viewpoints. The trip planning system may determine, based on the viewpoint data and the trip data, paths for the trip and driving times for the paths. The trip planning system may calculate an optimized path, from the paths, based on the viewpoint relevance scores and the driving times associated with the paths. The trip planning system may perform one or more actions based on the optimized path.