Traffic Movement Profile Generation via Anonymized Feature Matching
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Solution Overview
Problem
Existing methods for generating movement profiles of traffic participants in a traffic network face challenges in ensuring data protection and efficiency, particularly in determining the plausibility of feature data sets from different acquisition stations while maintaining non-personal identification and adhering to legal data protection requirements.
Innovation Solution
A method and system that determine the plausibility of matching feature data sets from multiple acquisition stations by considering location, time, and direction of travel, using a weighting function to evaluate feature data, allowing for the generation of movement profiles without personal identification, and ensuring data protection by only processing non-personal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data sets with feature data of traffic participants are collected from multiple acquisition stations to generate movement profiles, then the completeness and accuracy of traffic analysis is improved, but data protection requirements are compromised due to potential personal identification
Solution Approach 1:
The patent extracts only the necessary non-personal feature data from complete data sets, separating identifiable information from movement-relevant characteristics. This allows generation of movement profiles using only anonymized features such as vehicle color, type, and dimensional data while excluding personal identification information.
Solution Approach 2:
The patent applies partial action by processing only the portion of data sets that contains non-personal feature data relevant to movement profiles. Not all collected data is processed - only the anonymized features are used, leaving personal identification data unprocessed and protected.
2Measurement precision
If plausibility determination using location, time, and direction information is implemented, then the accuracy of matching feature data sets is improved, but the complexity of the evaluation process increases
Solution Approach 1:
The patent segments the plausibility determination into distinct evaluation components: location-based plausibility, time-based plausibility, and direction-based plausibility. Each component is evaluated separately using specific weighting factors, making the complex process more manageable and systematic.
Solution Approach 2:
The patent changes parameters by introducing weighting factors for different plausibility criteria (location, time, direction). By adjusting these weights, the system can adapt the evaluation process to different traffic network conditions without fundamentally changing the evaluation architecture.
3Reliability
If weighting functions are used to evaluate feature data similarity, then the reliability of matching ambiguous feature data is improved, but the computational requirements increase
Solution Approach 1:
The patent applies local quality by using different weighting factors for different feature data types based on their reliability and importance. Not all features are treated equally - certain features receive higher weights based on their local quality and significance for movement profile generation.
Solution Approach 2:
The patent uses simplified weighting functions that provide sufficient reliability without excessive computational complexity. Rather than implementing complex machine learning models, the system uses straightforward weighted evaluation that is computationally efficient while maintaining adequate reliability for traffic analysis.
4Object-affected harmful factors
If only non-personal feature data is processed for movement profiles, then data protection compliance is improved, but the amount of available data for complete traffic monitoring is reduced
Solution Approach 1:
The patent makes the system universally applicable by processing only non-personal feature data that can be used for multiple purposes: movement profile generation, traffic flow analysis, and congestion detection. This single approach serves multiple traffic management functions without requiring personal identification.
Solution Approach 2:
The patent introduces non-personal feature data as an intermediary between complete personal data and anonymized aggregates. This intermediary layer preserves sufficient information for traffic analysis while maintaining data protection, acting as a mediator that satisfies both monitoring needs and privacy requirements.
Data Source
AI summary
Method and system for generating movement profiles of traffic participants in a traffic network with at least two acquisition stations, wherein image data of the traffic participants are acquired by means of sensors of the acquisition stations and evaluated by means of an evaluation device, wherein data sets with feature data of the traffic participants are generated from the image data and wherein the data sets of different acquisition stations are compared.