Road Traffic Sensor Data Filtering and Error Correction
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Solution Overview
Problem
Existing road traffic sensor networks provide inaccurate and unreliable data due to faulty sensors, temporary transmission issues, and lack of operational status reporting, which complicates the determination of accurate traffic conditions.
Innovation Solution
A system that filters and conditions data samples from road traffic sensors and mobile data sources to detect and correct errors, using techniques like neural networks and Bayesian classifiers, and aggregates data from healthy sensors to estimate traffic conditions even in the absence of reliable data from individual sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic sensors are deployed to provide detailed traffic condition information, then measurement precision is improved, but reliability deteriorates due to sensor failures and transmission problems
Solution Approach 1:
The system divides the traffic sensor network into individual sensor entities, each with its own health status monitoring. By segmenting the data collection and processing architecture, the system can isolate failures to individual sensors without affecting the entire network, allowing continued operation using data from healthy sensors.
Solution Approach 2:
The system implements continuous feedback mechanisms where sensor health status is monitored and fed back to the data processing system. This feedback loop enables real-time detection of sensor failures and transmission problems, allowing the system to automatically adjust data collection strategies to maintain reliability while preserving measurement precision through selective use of reliable sensors.
2Device complexity
If sensor data is collected without health status monitoring, then device complexity is reduced, but reliability deteriorates due to inability to detect erroneous data
Solution Approach 1:
Sensors perform self-diagnosis and self-reporting of their operational status without requiring external monitoring systems. Each sensor independently monitors its own health and communicates this status information, eliminating the need for complex centralized health monitoring infrastructure while maintaining high data reliability through automatic exclusion of faulty sensor data.
3Quantity of substance
If erroneous sensor data is included in analysis, then data quantity is maintained, but measurement precision deteriorates due to inaccurate readings
Solution Approach 1:
The system extracts and removes erroneous data points along with their associated sensors from the analysis dataset. By identifying and separating faulty data based on health status monitoring, the system maintains sufficient data quantity for meaningful analysis while ensuring measurement precision is not compromised by inaccurate readings.
Solution Approach 2:
The system dynamically changes the inclusion criteria for data based on sensor health parameters. When a sensor is detected as unhealthy, the system modifies the data selection parameters to exclude its readings while continuing to process data from healthy sensors. This parameter adjustment maintains data volume sufficiency while preserving measurement accuracy.
Data Source
AI summary
Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from road traffic sensors (e.g., physical sensors that are near or embedded in the roads) and/or from vehicles and other mobile data sources traveling on the roads. The assessment of road traffic conditions based on obtained sensor data readings and/or other data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest. Assessing obtained data may further include determining traffic conditions (e.g., traffic flow and/or average traffic speed) for various portions of a road network in a particular geographic area, based at least in part on obtained data samples.