Context-Sensitive Route Planning Using Dynamic Weighted Graphs
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Solution Overview
Problem
Conventional route planning applications fail to provide context-dependent and user-preference-based driving directions, assuming constant road conditions and ignoring time of day, day of week, weather, and individual preferences, which leads to suboptimal route suggestions.
Innovation Solution
A route planning system that collects and analyzes contextual data and user preferences to generate dynamic driving directions by utilizing a weighted graph representation of the traffic system, where edges and nodes are adjusted based on real-time traffic conditions and user preferences, allowing for personalized route suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional route planning applications use static algorithms assuming constant road conditions, then the system complexity is low and ease of operation is maintained, but the adaptability to different contexts and user preferences deteriorates
Solution Approach 1:
The patent implements dynamic route planning by making the routing system adaptive to changing conditions. The system dynamically adjusts route recommendations based on real-time context (time of day, day of week, weather) and user preferences, transforming a static algorithm into a dynamic one that responds to environmental changes and user-specific requirements.
Solution Approach 2:
The patent changes multiple parameters simultaneously to achieve adaptability: temporal parameters (time of day, day of week), environmental parameters (weather conditions), and user-specific parameters (preferences, historical behavior). By varying these parameters, the system generates context-sensitive route recommendations rather than relying on fixed algorithms.
2Measurement precision
If route planning applications collect and analyze contextual data and user preferences, then the personalization and accuracy of route suggestions improve, but the loss of time for data processing and analysis increases
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing user preference data, historical route information, and contextual patterns before actual route planning is needed. This allows the system to have user profiles and preference settings already established, reducing the processing time required during actual route requests while maintaining high personalization accuracy.
Solution Approach 2:
The system implements feedback mechanisms where user responses to route suggestions and actual travel behavior are continuously monitored and fed back into the system. This feedback loop refines the accuracy of route suggestions over time while the system learns user preferences, reducing the need for extensive real-time analysis of raw data.
3Loss of information
If route planning applications provide comprehensive contextual information and user-specific recommendations, then the information quality and usefulness improve, but the quantity of information provided increases leading to information overload
Solution Approach 1:
The patent applies local quality by providing different levels and types of information tailored to specific user needs and contexts. Rather than providing uniform comprehensive information to all users, the system customizes the information presented based on individual user preferences, historical behavior, and specific routing contexts, ensuring each user receives appropriately targeted information.
Solution Approach 2:
The system implements partial action by selectively providing only the most relevant contextual information and route options to each user, rather than presenting all possible data. This filtering approach prevents information overload while maintaining high quality and usefulness by focusing on the subset of information most valuable to each specific user context.
Data Source
AI summary
A route-planning system is described that leverages a database of observations about routes taken by drivers in a region to generate context and/or preference sensitive routes. Contextual information such as time of day and day of week, along with such findings as the observed velocities on different roads and the efficiency of trips is noted from the database of trips to inform a route generation component. The route-generation component considers velocities, contextual information, and other findings to compute preferred routes for people requesting directions from a first geographical point to a second geographical point. In one usage, properties of a driver's own prior routes are used to generate personalized routes, including routes between previously unobserved starting and ending locations. In another application, sets of observed routes of other drivers are used in a collaborative manner to generate recommended routes for a specific driver based on inferred preferences of the driver.