Mobile Data Filtering for Road Traffic Assessment
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Solution Overview
Problem
Current methods for obtaining and assessing road traffic conditions are limited by the lack of accurate and timely data, especially in areas without road sensors, and the raw, disaggregated nature of available information, which hinders effective traffic management and decision-making.
Innovation Solution
A system that filters and conditions data from mobile sources and sensors to provide accurate, real-time traffic information by associating data samples with road segments, identifying outliers, and combining data from vehicles and sensors to assess traffic conditions, including speed and flow, for roads without functioning sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road sensor networks are deployed to obtain traffic condition data, then measurement precision and data availability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses mobile devices (cell phones, GPS units) as intermediary data collection points instead of deploying extensive road sensor networks. These mobile devices capture location and traffic condition data, which is then aggregated and processed to infer traffic conditions, thereby avoiding the complexity of installing and maintaining physical sensor infrastructure while still obtaining accurate traffic information.
Solution Approach 2:
The patent creates a virtual representation of traffic conditions by collecting and processing data from mobile devices that traverse the road network. Instead of physically measuring traffic at fixed sensor points, the system copies traffic condition information from multiple moving sources and synthesizes it into comprehensive traffic assessments, eliminating the need for physical sensor deployment.
2Quantity of substance
If data from multiple mobile sources is collected to improve coverage, then data quantity increases, but data quality and reliability deteriorate due to noise and outliers
Solution Approach 1:
The patent extracts and removes outlier data points and noise from the collected mobile device data using statistical analysis and filtering algorithms. By identifying and eliminating unreliable data points while retaining the bulk of useful information, the system maintains high data volume for comprehensive coverage while ensuring data reliability through selective removal of problematic entries.
Solution Approach 2:
The system implements feedback mechanisms where collected data is continuously analyzed, and the results are used to refine filtering criteria and improve future data collection. The system learns from patterns in the data to better identify outliers and adjust its processing algorithms, thereby maintaining reliability while preserving the benefits of large data volumes.
3Speed
If real-time traffic data processing is implemented, then responsiveness and timeliness are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the traffic data processing into distinct modular components: data collection from mobile devices, data filtering and outlier removal, traffic condition inference, and result delivery. Each segment handles specific tasks independently, allowing parallel processing and reducing overall computational complexity while maintaining real-time processing capabilities through efficient division of labor.
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 filtering of the data samples includes identifying data samples that are inaccurate or otherwise unrepresentative of actual traffic condition characteristics, such as data samples that are not of interest based at least in part on roads with which the data samples are associated and/or that otherwise reflect vehicle locations or activities that are not of interest.