Road-Network Graph Mapping for Accurate Vehicle Trip Analysis
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Solution Overview
Problem
Existing navigation systems fail to accurately match GPS data to specific road segments, struggle with differentiating between closely proximate roads, and are inadequate in handling large volumes of vehicle trip data for traffic pattern analysis.
Innovation Solution
A method and system that accurately associate GPS data with road segments by creating a graph representation of a road network, allowing for the aggregation and analysis of telematics data to identify popular routes, predict speed limits, and detect popular stops, using techniques that are computationally efficient and cost-effective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle trip data is collected and processed to provide navigation services, then service quality and user experience are improved, but data privacy concerns and security risks increase
Solution Approach 1:
The patent extracts and removes personally identifiable information from vehicle trip data through anonymization techniques. The system separates sensitive personal data from useful navigation patterns, retaining only aggregated, anonymized data for processing. This extraction principle resolves the contradiction by eliminating privacy risks while preserving service quality through the use of de-identified data.
Solution Approach 2:
The patent introduces an intermediary layer of aggregation and anonymization between raw vehicle trip data and navigation service processing. This intermediary process transforms individual vehicle data into aggregated traffic flow patterns, serving as a mediator that protects privacy while enabling service improvement. The intermediary layer ensures that no individual vehicle's location or behavior can be traced back to a specific user.
2Measurement precision
If detailed vehicle trip data is stored and analyzed, then route prediction accuracy and traffic analysis are improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The patent segments vehicle trip data into discrete, standardized data elements that can be processed independently. By dividing continuous trajectory data into segmented position points with standardized attributes (timestamp, location, speed), the system reduces processing complexity while maintaining prediction accuracy. Each segment can be processed separately through automated algorithms.
Solution Approach 2:
The patent transforms raw vehicle trip data into standardized parameters and features that simplify processing. By converting complex trajectory data into standardized parameters such as aggregated traffic flow metrics, speed patterns, and route frequency statistics, the system reduces data complexity while preserving the information needed for accurate route prediction and traffic analysis.
3Measurement precision
If real-time traffic data is processed to provide dynamic routing, then navigation accuracy is improved, but computational load and energy consumption increase
Solution Approach 1:
The patent performs preliminary processing of vehicle trip data to pre-compute traffic patterns, route statistics, and aggregation metrics during periods of lower computational demand. By pre-processing and storing aggregated traffic data in advance, the system reduces the computational load required during real-time navigation operations, thereby lowering energy consumption while maintaining navigation accuracy.
Solution Approach 2:
The patent applies partial processing to vehicle trip data by selectively analyzing only the most relevant data elements for current navigation needs. Rather than processing all available data in real-time, the system focuses on key parameters such as current traffic flow, recent route patterns, and immediate congestion conditions, reducing computational load and energy consumption while maintaining sufficient navigation accuracy.
Data Source
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AI summary
Systems and methods for associating vehicle trip data with a base map are provided herein. Systems and methods for providing vehicle trip data showing the greatest usage of routes between an origin and destination are also provided. Systems and methods for predicting a speed limit of a road based on vehicle trip data are also provided. Systems and methods for providing vehicle trip data showing popular stops between an origin and a destination are also provided. Systems and methods for providing contiguous region identification based on vehicle trip data are also provided.