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Highway

Ensuring the safety and sustainability of highways is paramount for a reliable transportation system.  Real-time monitoring with DFOS plays a critical role by detecting and addressing infrastructure issues promptly, minimizing accidents and disruptions for both drivers and commuters.

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Highway monitoring with DFOS

Traditional traffic monitoring systems often have cost and efficiency limitations. Distributed Fiber Optic Sensing (DFOS) offers a revolutionary solution that transforms highways into intelligent networks with real-time information. Using fiber optics laid along the roadways, DFOS monitors the signals generated by traffic flow.

DFOS excels at identifying and interpreting these signals, providing comprehensive knowledge of highway conditions. From routine vehicle movements to potential anomalies, DFOS captures a wealth of data. This translates into significant improvements in operational safety. Real-time information on traffic flow and potential hazards enables proactive responses to incidents, minimizing accidents and disruptions.

Efficient alarms

Another key benefit is increased efficiency. DFOS data allows traffic management to be optimized by being able to identify traffic jams, incidents, and accidents. In addition, the ability to locate areas of wear and tear facilitates targeted maintenance, maximizing infrastructure life and cost savings.

Beyond the immediate benefits, DFOS enables data collection for further analysis. Traffic patterns and trends can be identified, enabling informed decisions on infrastructure improvements and future developments. This data-driven approach paves the way for a smarter and more efficient highway network.

In conclusion, DFOS represents a strategic investment for highway monitoring. It promotes a safer and more efficient transportation system, enables proactive maintenance, and provides valuable data for future planning.

Highway info

OUR DFOS SOLUTION

Use cases included:

Traffic flow monitoring:

  • Speed
  • Congestion
  • Slow moving vehicles

Incident detection:

  • Early identification of accidents, breakdowns, and other disruptions.

Optional use cases:

Infrastructure health:

  • Detection of road surface deterioration, potential potholes, and structural issues.

Wrong-way driver detection:

  • Alerts for vehicles traveling in the wrong direction.

Traffic jam

Car detection

Impact over motorway

Highway traffic lines

Vehicle wrong entry

Abnormally slow vehicle

Accident

Stopped vehicle

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Listen

The interrogation unit transforms the optical fiber into a sensor with thousands of microphones that constantly listens to what is going on around it.

Analyze

The processing unit analyses the information received and classifies it into the various basic events perfectly localized with an accuracy of several meters

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Report

The system groups the events and informs the user only of the alarms that are of interest to him according to his needs.

Highway monitoring with DFOS Brochure

Download our highway monitoring with DFOS bochure where you will find all the details of our solution.

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Case Studies

We only sell solutions that really work

Intelligent traffic management system for transport infrastructure

The intelligent traffic management system for transport infrastructure uses Distributed Acoustic Sensing (DAS) technology and allows operators to detect, locate and classify different events, such as accidents, traffic jams, vehicle breakdowns, etc. and act as quickly as possible to mitigate the impact as soon as possible.

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Distributed Optical Monitoring for Intelligent Transportation Infrastructure

Distributed Storage technology Acoustic Sensing meets the requirements for the implementation of an intelligent traffic management system, as it enables the identification, location and classification of different events (accidents, traffic jams, oncoming traffic, etc.) in a precise and real-time manner, allowing response time to be minimised and the safety of road users to be maximised.

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Data analysis and classification

The acquired signals of the HDAS are traces that represent the strain variation along the optical fibre. We accumulate the traces in a waterfall plot, that allows us to analyse the vibration both in time and in space. We use digital image processing techniques in order to enhance the significative patterns of interest, and to reduce the noise.

Then, depending on the application, we apply a number of image analysis techniques using algorithms and mathematical models to detect and recognise patterns. For some use cases, we have developed supervised machine learning models, for other use cases “classic” algorithms give very good results.

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