Digital Map Dissimilarity Remediation via Trace Data Aggregation
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Solution Overview
Problem
Digital maps used in network systems for route guidance become stale as road networks change, making it challenging to accurately reflect current conditions, leading to dissimilarities between map data and trace data from client devices.
Innovation Solution
A network system that aggregates trace data from client devices to generate ground truth data, compares it to digital maps, and remediates dissimilarities using statistical analysis, machine learning, and scoring, while also modifying methods and techniques to determine and display dissimilarities accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital maps are updated frequently to reflect road network changes, then map accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system enables self-service by automatically collecting trace data from client devices, processing it through statistical analysis and machine learning algorithms, and updating maps without manual intervention. The network system autonomously identifies dissimilarities, validates ground truth data, and remediates map inaccuracies through automated workflows.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing trace data against digital maps, identifying dissimilarities, and using this information to update and improve map accuracy. The automated validation and remediation processes create closed-loop feedback that progressively enhances map quality over time.
2Reliability
If trace data from multiple client devices is aggregated to verify road network conditions, then data reliability is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and aggregating trace data as it is collected from client devices, rather than waiting until verification is needed. Ground truth data is generated and validated in advance, reducing processing time when actual verification and map updates are required.
Solution Approach 2:
The system segments the large-scale data processing task into smaller, manageable components by dividing trace data aggregation into individual client device contributions, processing dissimilarity detection for specific road segments, and handling validation tasks separately. This modular approach improves processing efficiency and resource utilization.
3Measurement precision
If dissimilarity detection thresholds are lowered to identify more map inaccuracies, then detection precision is improved, but false positive rate increases
Solution Approach 1:
The system introduces an intermediary validation layer using ground truth data derived from aggregated trace data. Before confirming a dissimilarity as a true map error, the system validates findings against this intermediary ground truth, filtering out false positives while maintaining high detection precision for actual inaccuracies.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on statistical analysis of trace data patterns and machine learning model predictions. By adapting parameters to specific road segments, traffic conditions, and data quality metrics, the system optimizes the balance between detection sensitivity and false positive reduction.
Data Source
AI summary
A network system determines and remediates dissimilarities between a digital map and trace data of a road network in an area as service providers and service requesters coordinate service using the road network in the area. To determine dissimilarities the network system can select and convert a digital map, aggregate received trace data into trace data accurately representing the road network in the area, generate a visualization of the dissimilarities, and remediate the dissimilarities using a variety of methods. A first remediation method includes verifying the dissimilarity using a single service provider, a second method includes verifying the dissimilarity leveraging multiple service providers, and a third method modifies the methods used to determine dissimilarities. After remediation, the network system can generate a map of the area that accurately represents the road network in the area.