Mobile Data Traffic Assessment Using Sensor Fusion
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Solution Overview
Problem
Current methods for assessing road traffic conditions are limited by the lack of accurate and timely data, especially in areas without road sensors, and often provide raw, disaggregated information that is of limited utility.
Innovation Solution
A system that utilizes data samples from mobile sources, such as vehicles equipped with GPS and geo-location devices, combined with data from road sensors, to filter, condition, and assess traffic conditions in real-time, providing filtered data for use in predicting future traffic scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road sensors are installed in all areas, then measurement coverage is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces mobile data sources (vehicles, smartphones) as intermediary carriers that collect traffic data dynamically along road segments. These mobile sources act as temporary sensors that supplement fixed sensor networks, enabling comprehensive coverage without requiring sensors on every road segment. The mobile devices transmit data to a central processing system that integrates information from both fixed and mobile sources.
Solution Approach 2:
The patent makes mobile data sources serve multiple functions: they act as traffic sensors, mobile communication nodes, and data collection points simultaneously. Vehicles and smartphones already present in the environment are utilized for traffic monitoring, eliminating the need for dedicated fixed sensors in all areas. This multi-functional approach reduces overall system complexity while maintaining comprehensive coverage.
2Quantity of substance
If road sensors are installed, then traffic data availability is improved, but reliability decreases due to sensor failures and data accuracy issues
Solution Approach 1:
The patent implements feedback mechanisms where mobile data sources continuously report their status and measurements to the central system. The system monitors data quality in real-time and can request re-transmissions or corrections when anomalies are detected. This feedback loop enables continuous improvement of data reliability while maintaining high availability through the distributed nature of mobile sources.
Solution Approach 2:
The patent dynamically adjusts data collection parameters based on environmental conditions and data quality assessments. When sensor failures or poor data quality are detected, the system changes parameters such as increasing sampling frequency, adjusting mobile device reporting intervals, or switching to alternative data sources. This adaptive parameter adjustment maintains data reliability without compromising availability.
3Quantity of substance
If traffic information is provided in raw form, then data volume is improved, but utility decreases due to lack of processing and analysis
Solution Approach 1:
The patent extracts meaningful information from raw mobile data by identifying and separating key traffic parameters such as speed, location, direction, and temporal patterns. The system extracts relevant features from the abundant raw data and presents them in processed formats that directly address user needs. This extraction process transforms voluminous raw data into actionable insights without losing information value.
Solution Approach 2:
The patent segments the complex task of data processing into distinct functional modules: data collection, data validation, data aggregation, analysis, and presentation. Each module handles specific aspects of information transformation, making the overall system more manageable and efficient. This segmentation allows raw data to be systematically processed into useful information while maintaining traceability to original sources.
4Loss of time
If real-time traffic data is collected from mobile sources, then timeliness is improved, but device complexity increases due to data processing requirements
Solution Approach 1:
The patent performs preliminary data processing actions at the mobile data sources themselves, where devices filter, validate, and pre-process data locally before transmission to the central system. This preliminary action reduces the volume and complexity of data requiring central processing, enabling real-time collection without overwhelming processing requirements. Mobile devices perform basic cleaning and formatting operations in advance.
Solution Approach 2:
The patent applies partial processing at mobile sources and comprehensive processing at the central system, distributing the processing workload strategically. Mobile devices perform only the necessary minimal processing to ensure data readiness, while the central system handles the bulk of complex analysis and aggregation. This partial action approach maintains real-time responsiveness while managing overall system complexity through functional distribution.
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 vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics from the data samples. In some situations, the inferences based on the data samples includes repeatedly determining traffic flow characteristics for road segments of interest during periods of time, such as to determine traffic volume and/or average occupancy of the road.