Dynamic Road Traffic Noise Mapping Using Distributed Fiber Optic Sensing
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Solution Overview
Problem
Current road traffic noise models are unable to predict noise levels in real-time and do not account for varying wind speeds, assuming constant speed and neutral atmospheric conditions, which limits their accuracy in noise mapping and abatement planning.
Innovation Solution
Dynamic road traffic noise mapping using distributed fiber optic sensing (DFOS) systems over a telecommunications network, which provides real-time data on vehicle speed, volume, and type, and incorporates wind speed adjustments to predict sound pressure levels accurately, enabling precise noise mapping at any observer location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current noise models use constant speed assumptions, then model simplicity is maintained, but real-time noise prediction capability is lost
Solution Approach 1:
The patent transforms the static noise prediction model into a dynamic system by continuously updating vehicle speed parameters using real-time data from DFOS. The system monitors actual vehicle speeds along road segments and dynamically adjusts noise predictions, enabling real-time noise mapping while maintaining model structure through systematic data integration.
Solution Approach 2:
The system implements feedback by continuously measuring actual vehicle speeds using DFOS and feeding this information back into the noise prediction model. This closed-loop approach allows the model to self-correct and adapt to real-time traffic conditions, achieving real-time prediction capability without requiring complete model restructuring.
2Device complexity
If neutral atmospheric conditions are assumed, then model complexity is reduced, but accuracy in varying wind conditions deteriorates
Solution Approach 1:
The system uses DFOS technology to self-measure wind speed and direction along the road corridor. By leveraging the existing fiber optic infrastructure for both communication and environmental sensing, the model automatically obtains atmospheric conditions without requiring external weather stations, maintaining simplicity while improving accuracy.
Solution Approach 2:
The patent makes the fiber optic network multi-functional by using it simultaneously for telecommunications and environmental sensing (wind speed, temperature, humidity). This universal approach allows the noise prediction model to access atmospheric data through the same infrastructure used for data transmission, avoiding additional complex sensing systems.
3Quantity of substance
If traditional noise monitoring methods are used, then infrastructure cost is reduced, but real-time traffic data acquisition capability is insufficient
Solution Approach 1:
The patent merges telecommunications infrastructure with environmental sensing functions. By integrating DFOS capability into existing fiber optic networks, the system combines data communication and noise/traffic monitoring into a single unified infrastructure, eliminating the need for separate traditional monitoring equipment while enabling real-time data acquisition.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for real-time, accurate noise level predictions and mapping, improving noise abatement planning by incorporating real-time traffic data and wind speed adjustments, enhancing the accuracy of noise modeling beyond existing limitations.
Implementation Method 1
utilize DFOS to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber
Implementation Method 2
obtain real-time wind speed using DFOS such as distributed acoustic sensing (DAS) to provide sound pressure adjustment due to the wind speed
Data Source
AI summary
Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.