Personalized Route Planning Using Travel Patterns
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Solution Overview
Problem
Existing route planning systems fail to provide personalized routes that cater to individual user preferences and patterns, often prioritizing efficiency over user-specific requirements such as avoiding unsafe areas or preferred travel routes.
Innovation Solution
A personalized route planning system that modifies efficient routes based on multiple datasets, including user travel patterns, accident frequencies, crime statistics, and user preferences, to generate routes that align with the user's characteristic travel habits while allowing for increased time or distance, and optionally avoiding predetermined areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system prioritizes time and distance efficiency in route planning, then the route calculation speed and efficiency are improved, but the user satisfaction and personalization are worsened
Solution Approach 1:
The patent segments the route planning process into multiple independent components: efficient route calculation, personalized route generation, and pattern-based route creation. Each segment handles specific aspects (time/distance optimization, user preference integration, travel pattern incorporation) that can be computed separately and then combined, allowing efficient processing while delivering personalized results.
Solution Approach 2:
The patent adds a new dimension to traditional route planning by incorporating user-specific dimensions such as characteristic travel patterns, preferred areas, avoided areas, and safety concerns. This transforms the route planning from a two-dimensional problem (time and distance) to a multi-dimensional problem that includes personalization factors, enabling simultaneous optimization of efficiency and personalization.
2Adaptability or versatility
If the system incorporates multiple datasets and user patterns to create personalized routes, then the user satisfaction is improved, but the computational complexity and processing time are worsened
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing user characteristic travel patterns, preferred areas, and avoided areas during periods when routing is not actively requested. This preprocessing of personalization data reduces the computational burden during actual route planning, as the system only needs to query and apply pre-analyzed user preferences rather than analyzing raw data from scratch each time.
Solution Approach 2:
The patent introduces intermediary data structures such as characteristic travel patterns and area classifications that act as mediators between raw user data and route calculation algorithms. These intermediaries simplify the complexity by providing structured, pre-processed information that can be efficiently applied to route planning without requiring complex real-time analysis of all user preferences.
3Reliability
If the system offers personalized routes that avoid unsafe areas or include preferred areas, then the user safety and comfort are improved, but the route time and distance are worsened
Solution Approach 1:
The patent changes the optimization parameters from purely time and distance efficiency to include multiple weighted factors such as safety, user preferences, and travel patterns. By adjusting these parameters, the system can generate routes that prioritize safety and comfort over strict efficiency, allowing users to make informed decisions about their specific needs for each journey.
Data Source
AI summary
The personalized route planning system modifies a time or distance efficient route to a destination in response to a multiple of datasets to determine one or more personalized routes to the destination.