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Edge Computing for Effective and Efficient Traffic Characterization.

Asif Khan1, Khurram S Khattak2, Zawar H Khan1

  • 1Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.

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Summary
This summary is machine-generated.

This study introduces a low-cost edge computing system for smart urban mobility, analyzing nine traffic parameters for enhanced traffic flow analysis. The solution offers accurate vehicle detection and speed estimation, improving urban traffic management.

Keywords:
Internet of ThingsRaspberry Piedge computingtraffic monitoringurban mobilityvehicle detection

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Area of Science:

  • Urban planning and intelligent transportation systems.
  • Computer vision and edge computing applications.
  • Real-time traffic data analysis.

Background:

  • Effective traffic flow analysis is crucial for developing smart urban mobility solutions.
  • Existing traffic monitoring tools often rely on a limited number of parameters.
  • There is a need for comprehensive, low-cost, and robust traffic monitoring systems.

Purpose of the Study:

  • To propose an edge computing solution for comprehensive traffic flow analysis using nine key parameters.
  • To develop a low-cost, easily deployable, and maintainable sensor node for traffic monitoring.
  • To evaluate the accuracy and operational feasibility of the proposed system in real-world conditions.

Main Methods:

  • An edge computing sensor node was developed using Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, and 4G connectivity.
  • Pre-trained models from the OpenVINO Toolkit were utilized for vehicle detection and classification.
  • A centroid tracking algorithm was implemented for vehicle speed estimation, with data transmitted to the ThingSpeak cloud platform.
  • Field testing was conducted over one week, analyzing approximately 10,000 vehicles daily.

Main Results:

  • The system achieved accuracies of 79.8% for vehicle count, 93.2% for classification, and 82.9% for speed estimation.
  • The sensor node demonstrated operational capability for approximately 8 hours using a 10,000 mAh power bank.
  • The required data bandwidth was measured at 1.5 MB/h, indicating efficient data transmission.

Conclusions:

  • The proposed edge computing solution effectively overcomes the limitations of existing traffic monitoring systems by incorporating multiple parameters.
  • The system is a low-cost, robust, and accurate solution suitable for smart urban mobility and can operate in challenging environments.
  • This approach provides a scalable and efficient method for real-time traffic analysis and management.