Traffic Detector Abnormality Detection via Statistical Map Comparison
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Solution Overview
Problem
Existing traffic control systems face challenges in quickly and accurately detecting abnormalities in vehicle detectors, which can lead to decreased system performance and affect traffic management decisions.
Innovation Solution
An abnormality detection device that collects detector information, generates map information representing abnormal states, and determines the status of vehicle detectors using statistical processing and categorization, allowing for high-accuracy detection without requiring additional collection devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used where users manually analyze traffic situations, then system complexity is reduced, but abnormality detection speed and accuracy deteriorate
Solution Approach 1:
The system enables self-service abnormality detection by automatically comparing detector measurement quantities against map information representing normal traffic patterns. The detector state determiner autonomously identifies abnormalities without requiring manual user analysis, allowing the system to detect detector failures, traffic pattern changes, and data quality issues independently.
Solution Approach 2:
The patent replaces manual mechanical monitoring with automated electronic detection. Instead of users visually analyzing traffic situation data, the system uses computer-based comparison algorithms that automatically evaluate measurement quantities against stored map information, substituting human analysis with automated computational processes.
2Measurement precision
If additional collection devices are deployed to improve detection accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The system creates virtual copies of detector information by generating map information that represents normal traffic patterns from historical data. Instead of deploying additional physical detectors, the system uses software-based virtual representations to compare against actual measurements, achieving high detection precision without adding physical collection devices.
Solution Approach 2:
The existing detector infrastructure serves multiple functions: it collects traffic volume data for normal traffic management and simultaneously provides measurement quantities for abnormality detection when compared against map information. This multi-functional use of existing devices eliminates the need for separate detection equipment.
3Loss of time
If real-time detection is implemented to quickly identify abnormalities, then response time improves, but processing complexity and computational load increase
Solution Approach 1:
The system performs preliminary action by pre-generating map information that represents normal traffic patterns before actual detection is needed. This pre-computed reference data is stored and ready for immediate comparison with real-time measurements, enabling rapid abnormality detection without complex real-time computational analysis.
Solution Approach 2:
The detection process is segmented into distinct functional components: the map information generator creates reference patterns from historical data, while the detector state determiner performs real-time comparison operations. This segmentation allows each component to be optimized independently, with the comparison stage requiring minimal computational resources for fast real-time operation.
4Reliability
If comprehensive detector monitoring is implemented to ensure system reliability, then traffic control reliability improves, but operational complexity increases
Solution Approach 1:
The monitoring system performs self-service by automatically comparing detector measurements against map information and identifying abnormalities without requiring user intervention. The detector state determiner autonomously evaluates detector functionality, traffic pattern consistency, and data quality, eliminating the need for users to manually analyze complex traffic situations.
Solution Approach 2:
The system implements feedback by continuously comparing actual detector measurements with expected values from map information and using this comparison to identify abnormalities. This automated feedback mechanism provides continuous monitoring that maintains system reliability while simplifying operations, as the system self-regulates and alerts users only when actual measurements deviate from normal patterns.
Data Source
AI summary
An abnormality detection device of an embodiment includes a traffic control center device to detect an abnormality of vehicle detectors installed in a road network, including: a first traffic management unit that collects detector information including measurement quantities from the vehicle detectors; a statistical processing unit that statistically processes the measurement quantities for each designated period to generate statistical information including statistical values of the measurement quantities; a map information generating unit that generates, based on the statistical values of the measurement quantities of the vehicle detectors that already have been determined to be abnormal by a user, map information representing a distribution situation of the statistical values of the measurement quantities in an abnormal state; and a detector state determination unit that determines, based on the measurement quantities of each vehicle detector to be assessed and the map information in the abnormal state, whether the vehicle detector is abnormal.