Edge computing for effective and efficient traffic characterization
| dc.contributor.author | Khan, Asif | |
| dc.contributor.supervisor | Gulliver, T. Aaron | |
| dc.contributor.supervisor | Khan, Zawar | |
| dc.date.accessioned | 2024-06-20T17:30:07Z | |
| dc.date.available | 2024-06-20T17:30:07Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Applied Science MASc | |
| dc.description.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 Centroid Tracking Algorithm (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 complex and heterogeneous environments. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.bibliographicCitation | Khan, A.; Khattak, K.S.; Khan, Z.H.; Gulliver, T.A.; Abdullah. Edge Computing for Effective and Efficient Traffic Characterization. Sensors 2023, 23, 9385. https://doi.org/10.3390/s23239385 | |
| dc.identifier.uri | https://hdl.handle.net/1828/16636 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Edge Computing | |
| dc.subject | Traffic Characterization | |
| dc.subject | Artificial Intelligence | |
| dc.title | Edge computing for effective and efficient traffic characterization | |
| dc.type | Thesis |