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Topic

Edge Computing for Effective and Efficient Traffic Characterization

Department of Electrical and Computer Engineering

Date & location

• Wednesday, May 22, 2024

• 11:00 A.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. T. Aaron Gulliver, Department of Electrical and Computer Engineering, ·¬ÇÑÉçÇø (Co-Supervisor)

• Dr. Zawar Khan, Department of Electrical and Computer Engineering, UVic (Co-Supervisor) 

External Examiner

• Dr. Phalguni Mukhopadhyaya, Department of Civil Engineering, ·¬ÇÑÉçÇø 

Chair of Oral Examination

•  Dr. Gina Harrison, Department of Educational Psychology and Leadership Studies, UVic

Abstract

Traffic flow analysis is essential to develop smart urban mobility solutions. Many advanced traffic flow monitoring solutions have been proposed but they employ only a small number of parameters. To overcome this limitation, an edge computing solution is proposed based on nine traffic parameters, namely, vehicle count, direction, speed, and type, flow, peak hour factor, density, time headway, and distance headway. This solution is low cost, low power, low data bandwidth, and easy to install, deploy and maintain. It is a sensor node comprised of an RPi 4, Pi Camera, Intel Movidius NCS2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a CRA is used to estimate vehicle speed. The measured traffic parameters are transmitted to the ThingSpeak cloud platform via 4G. The proposed solution was field-tested for one week (7 h/day), with approximately 10,000 vehicles per day. The count, classification, and speed accuracies obtained were 79.8%, 93.2%, and 82.9%, respectively. The sensor node can operate for approximately 8 h with a 10,000 mAh power bank and the required data bandwidth is 1.5 MB/h. The proposed edge computing solution overcomes the limitations of existing traffic monitoring systems and can work in hostile environments.