Road Traffic Assessment Using Mobile Data Mediators
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Solution Overview
Problem
Current methods for determining road traffic conditions are limited by inaccurate and unreliable data from traffic sensors, lack of coverage in areas without sensors, and inability to predict future traffic conditions effectively, leading to inadequate information for planning and decision-making.
Innovation Solution
A system that assesses road traffic conditions using data from vehicles and mobile data sources, filters and conditions the data to correct errors, and generates predictions of future traffic conditions by modeling road networks and incorporating various data sources, including sensors and mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic sensors are used to measure traffic conditions, then detailed traffic information can be obtained, but the data becomes inaccurate and unreliable due to sensor failures and transmission problems
Solution Approach 1:
The patent uses mobile devices as intermediary data collection points between traffic sensors and the central processing system. These mobile devices act as mediators that can verify sensor data, provide alternative measurements, and bridge gaps when sensors fail, thereby improving both accuracy and reliability of traffic condition information.
Solution Approach 2:
The system implements feedback mechanisms where mobile device data is used to validate and correct sensor readings. By continuously comparing data from multiple sources and allowing corrections based on observed patterns, the system improves data reliability and identifies when sensor failures occur.
2Area of stationary object
If road sensors are installed in all areas, then complete traffic coverage is achieved, but the system complexity and cost increase significantly
Solution Approach 1:
The patent makes mobile devices universal data collection tools that can function as temporary or auxiliary sensors. These devices perform multiple functions: collecting traffic data, validating sensor readings, providing backup information, and filling coverage gaps. This eliminates the need for permanent sensor installation in every area while maintaining comprehensive monitoring capability.
Solution Approach 2:
The system transitions from a static sensor network to a dynamic data collection approach where mobile devices can be deployed flexibly based on real-time needs. The system adapts its data collection strategy dynamically, using mobile devices to fill gaps when sensor coverage is insufficient or when specific areas require enhanced monitoring.
3Loss of information
If historical traffic data is used for analysis, then patterns can be identified, but the ability to predict future conditions accurately is limited
Solution Approach 1:
The system performs preliminary data collection and validation using mobile devices before the data is needed for prediction. By continuously gathering and pre-processing data from multiple sources, the system builds up a comprehensive dataset that can be immediately used for real-time predictions without time lags, improving both the accuracy of future condition predictions and the timeliness of available information.
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 and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment 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 of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).