Personalized Travel Time Estimation Using Driver Profile Correction
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Solution Overview
Problem
Conventional navigation systems provide inaccurate estimated travel times and arrival times due to their reliance on aggregated data, failing to account for individual drivers' habits and styles, which results in imprecise estimates for specific drivers.
Innovation Solution
A method and apparatus that receive route information data records to establish an estimated time of arrival value and determine a personalized correction value based on road elements, maneuvers, and a driver's profile, allowing for a personalized estimation of travel time that reflects individual driving habits without compromising privacy by not using geographic-specific information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional navigation systems use aggregated information from multiple drivers to estimate travel time, then the system maintains privacy and uses minimal data storage, but the estimation accuracy for individual drivers deteriorates
Solution Approach 1:
The patent segments the travel time estimation into two distinct components: a base estimated travel time derived from aggregated traffic data, and a personalized correction value derived from individual driver behavior patterns. This segmentation allows the system to maintain both privacy benefits of aggregation and accuracy benefits of personalization without requiring complete data centralization.
Solution Approach 2:
The system applies local quality by making the estimation personalized to each driver's specific characteristics and driving patterns. The correction value is locally adapted to individual driver behavior, while the base estimation remains globally applicable. This allows accurate personalized estimates without requiring every driver's complete trip history to be stored centrally.
2Measurement precision
If conventional navigation systems aggregate data from multiple drivers, then privacy is partially protected, but the ability to provide personalized accurate estimates deteriorates
Solution Approach 1:
The patent extracts only the essential behavioral patterns needed for personalization while leaving detailed personal information localized. The correction value captures individual driver characteristics without requiring storage of complete trip histories, geographic locations, or personally identifiable information. This extraction approach provides personalization accuracy while minimizing privacy loss.
Solution Approach 2:
The correction value acts as an intermediary that bridges aggregated base estimates and personalized accuracy. Instead of directly using raw personal data, the system processes individual driver behavior through this intermediate correction mechanism, which encapsulates personalization needs without exposing detailed personal information. This intermediary layer protects privacy while enabling accurate personalized estimation.
3Measurement precision
If navigation systems collect detailed individual driver data for personalization, then estimation accuracy improves, but privacy protection deteriorates
Solution Approach 1:
The patent uses a lightweight correction value that can be computed from relatively limited individual data rather than requiring extensive long-term data collection. The correction value serves as a compact representation of driver behavior that provides personalization without the privacy risks associated with storing detailed, long-term personal travel histories. This approach achieves accuracy with minimal privacy exposure.
4Adaptability or versatility
If conventional systems use aggregated traffic data, then system complexity remains low, but the ability to account for individual driving habits deteriorates
Solution Approach 1:
The system dynamically adapts to individual driver characteristics through the correction value, which can be updated as new driving patterns are observed. This dynamic adaptation allows the system to become more personalized over time without requiring a complete redesign of the estimation architecture. The base estimation remains static and simple, while only the correction component requires dynamic personalization.
Data Source
AI summary
Route information data records are received from a navigation system indicating road elements and maneuvers between a starting point and a destination point. An estimated time of arrival is established using the route information data records. A personalized correction value based on the road elements, maneuvers, and a driver profile is determined. A personalized time of arrival is calculated based on the estimated time of arrival value and the personalized correction value.