Decentralized Traffic Event Database With Dynamic Retention
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Solution Overview
Problem
Existing traffic event detection systems fail to dynamically update information in real-time, leading to rigid storage of traffic models that do not account for the dynamics of traffic flow, making it difficult to identify recurring events at specific sections.
Innovation Solution
A method where traffic events are transmitted to a data network with associated position and time data, and each event is assigned an individual retention period, allowing for automatic deletion after expiration, with retention periods adjusted based on frequency and hazard factors, enabling a 'learning and forgetting' mechanism that prioritizes retention of frequent and hazardous events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traffic events are stored indefinitely in a centralized database, then information availability is improved, but data volume and system complexity increase
Solution Approach 1:
The patent implements decentralized storage where each vehicle maintains its own local database of traffic events rather than relying on a centralized repository. This distributes the data volume across multiple nodes while ensuring information availability through local access. The local quality principle is applied by making each vehicle's database self-sufficient for retrieving relevant traffic event information without requiring centralized storage.
Solution Approach 2:
The patent introduces dynamic retention periods for traffic events based on their hazard level and frequency of occurrence. Critical events with high hazard levels or frequent recurrence are retained longer in the network, while less important events are deleted sooner. This dynamic approach optimizes data volume by automatically adjusting storage duration based on the actual importance and relevance of each traffic event, preventing indefinite accumulation of all data.
2Loss of information
If all traffic events are retained with equal duration, then information completeness is improved, but relevance accuracy deteriorates
Solution Approach 1:
The patent changes the parameter of retention duration based on the hazard level and frequency characteristics of each traffic event. Instead of a fixed retention period, the system adjusts how long events are kept in the decentralized database according to their importance. High-hazard events like accidents or dangerous road conditions are retained longer, while minor events are deleted sooner, thereby maintaining relevance accuracy while preserving information completeness for critical events.
Solution Approach 2:
The system applies different retention qualities to different traffic events based on their local characteristics (hazard level, frequency). Each traffic event type receives a customized retention policy rather than uniform treatment, allowing the system to prioritize storage of the most relevant information while still maintaining a complete record of all events with appropriate differential retention periods.
3Reliability
If traffic information is updated frequently across the network, then information currency is improved, but communication overhead increases
Solution Approach 1:
The decentralized database system allows each vehicle to independently query and update its local copy of traffic event information without requiring centralized coordination for every update. Vehicles autonomously manage their local databases by receiving updates from other vehicles directly through peer-to-peer communication, reducing the communication overhead associated with centralized update mechanisms while maintaining information currency across the network.
4Loss of information
If critical traffic events are prioritized for retention, then information relevance is improved, but data uniformity deteriorates
Solution Approach 1:
The system applies parameter changes by adjusting the retention duration based on the hazard level and frequency of traffic events. Critical events with high hazard levels or frequent occurrence patterns are assigned longer retention periods, while less important events are retained for shorter durations. This creates a differentiated data structure where the same type of event can have different retention lifecycles based on its characteristics, prioritizing relevant information while accepting variable data composition across different event types.
Data Source
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AI summary
The invention relates to a method for learning traffic events (212, 213, 214, 215), said traffic events (212, 213, 214, 215) being transmitted to a data network using vehicle-to-X communication. The traffic events (212, 213, 214, 215) comprise position data and time data assigned to the traffic events (212, 213, 214, 215), and the traffic events (212, 213, 214, 215) are stored electronically in the data network. The method is characterized in that an individual storage duration is determined for each traffic event (212, 213, 214, 215), and the traffic event (212, 213, 214, 215) is deleted from the data network after the storage duration expires. The invention further relates to a corresponding system and to the use thereof.