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Long-Range Traffic Monitoring Based on Pulse-Compression Distributed Acoustic Sensing and Advanced Vehicle Tracking

Iñigo Corera1, Enrique Piñeiro1, Javier Navallas1

  • 1Institute of Smart Cities and Department of Electrical, Electronic and Communications Engineering, Universidad Pública de Navarra, 31006 Pamplona, Spain.

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Summary

A new long-range traffic monitoring system uses fiber-optic distributed acoustic sensing (DAS) for vehicle detection and classification. This novel system achieves high accuracy, classifying vehicles with up to 97.7% accuracy.

Keywords:
distributed acoustic sensingoptical pulse compressionoptical time domain reflectometrytraffic monitoringvehicle classification

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Area of Science:

  • Engineering
  • Sensor Technology
  • Transportation Systems

Background:

  • Traditional traffic monitoring systems face limitations in range and resolution.
  • Distributed Acoustic Sensing (DAS) offers potential for enhanced traffic monitoring capabilities.

Purpose of the Study:

  • To develop and validate a novel long-range traffic monitoring system using fiber-optic DAS.
  • To enable accurate vehicle detection, tracking, and classification over extended distances.

Main Methods:

  • Utilized an optimized DAS setup with pulse compression for high resolution and long-range sensing.
  • Developed a novel transformed domain algorithm, an evolution of the Hough Transform, for vehicle detection and tracking.
  • Implemented a machine learning algorithm for vehicle classification based on extracted vehicle signatures.

Main Results:

  • The system successfully monitored traffic over a 40 km stretch using dark fiber.
  • Achieved excellent classification rates: 97.7% for general vehicle passing events, 99.6% for cars, and 85.7% for trucks.
  • Demonstrated the effectiveness of pulse compression and the novel transformed domain algorithm in DAS traffic monitoring.

Conclusions:

  • The developed fiber-optic DAS system provides a robust and accurate solution for long-range traffic monitoring.
  • The novel algorithms and optimized setup represent a significant advancement in DAS applications for transportation.
  • This technology holds promise for improving traffic management and safety through precise vehicle analysis.