Anomaly Estimation for Traffic Hold-up Detection
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Solution Overview
Problem
Existing traffic prediction systems, such as Japanese Patent No. 3792172, face inefficiencies when calculating alternative routes to avoid traffic hold-ups, as they require exhaustive generation and evaluation of routes to determine if a hold-up has occurred, leading to slow calculations and increased processing load.
Innovation Solution
An anomaly estimation apparatus that collects vehicle data, calculates feature amounts, determines anomaly occurrence points, generates estimation data, and uses causality information to quickly estimate anomaly transitions and their influence, allowing for rapid calculation of alternative routes by associating anomaly occurrence points with their periphery points and analyzing causality between them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive route generation and evaluation is performed to determine traffic hold-up, then route accuracy is improved, but calculation time and processing load increase
Solution Approach 1:
The patent segments the route evaluation process into two stages: first, quickly identify candidate routes using simplified criteria; second, perform detailed hold-up detection only on these limited candidates. This segmentation reduces the number of routes requiring exhaustive evaluation while maintaining detection accuracy.
Solution Approach 2:
The patent applies partial action by performing complete hold-up detection only on a subset of routes (those meeting preliminary selection criteria) rather than all possible routes. This partial evaluation approach maintains sufficient accuracy for route recommendation while significantly reducing computational burden.
2Reliability
If exhaustive route generation is performed to avoid traffic hold-up, then route reliability is improved, but device complexity increases
Solution Approach 1:
The processing system is segmented into multiple functional modules: route generation module, preliminary filtering module, hold-up detection module, and recommendation module. Each module handles a specific task, reducing overall system complexity while maintaining reliable route recommendations through coordinated operation.
Solution Approach 2:
The system performs complete reliability checks (hold-up detection) only on routes that pass preliminary filtering, rather than all generated routes. This partial verification approach maintains sufficient route reliability for practical use while reducing processing complexity.
Data Source
AI summary
An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point.