Edge Computing for Effective and Efficient Traffic Characterization

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dc.contributor.authorKhan, Asif
dc.contributor.authorKhattak, Khurram S.
dc.contributor.authorKhan, Zawar H.
dc.contributor.authorGulliver, Thomas Aaron
dc.contributor.authorAbdullah
dc.date.accessioned2023-12-08T21:19:46Z
dc.date.available2023-12-08T21:19:46Z
dc.date.copyright2023en_US
dc.date.issued2023
dc.description.abstractTraffic flow analysis is essential to develop smart urban mobility solutions. Although numerous tools have been proposed, 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. The proposed low-cost solution is easy to deploy and maintain. The sensor node is comprised of a Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a centroid tracking algorithm 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.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.citationKhan, A., Khattak, K. S., Khan, Z. H., Gulliver, T. A., & Abdullah. (2023). Edge computing for effective and efficient traffic characterization. Sensors, 23(23), 9385. https://doi.org/10.3390/s23239385en_US
dc.identifier.uri https://doi.org/10.3390/s23239385
dc.identifier.urihttp://hdl.handle.net/1828/15691
dc.language.isoenen_US
dc.publisherSensorsen_US
dc.rightsAttribution 2.5 Canada*
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/ca/*
dc.subjectedge computing
dc.subjecttraffic monitoring
dc.subjecturban mobility
dc.subjectInternet of Things
dc.subjectRaspberry Pi
dc.subjectvehicle detection
dc.subject.departmentDepartment of Electrical and Computer Engineering
dc.titleEdge Computing for Effective and Efficient Traffic Characterizationen_US
dc.typeArticleen_US

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