MagMonitor: Vehicle Speed Estimation and Vehicle Classification Through A Magnetic Sensor

Feng, Y; Mao, G; Chen, B; Li, C; Hui, Y; Xu, Z; Chen, J

MagMonitor: Vehicle Speed Estimation and Vehicle Classification Through A Magnetic Sensor

Feng, Y

Mao, G Chen, B Li, C Hui, Y Xu, Z Chen, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2022, 23, (2), pp. 1311-1322
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Feng, Y https://orcid.org/0000-0002-3605-6829
dc.contributor.author	Mao, G
dc.contributor.author	Chen, B
dc.contributor.author	Li, C
dc.contributor.author	Hui, Y
dc.contributor.author	Xu, Z
dc.contributor.author	Chen, J
dc.date.accessioned	2023-04-05T01:00:48Z
dc.date.available	2023-04-05T01:00:48Z
dc.date.issued	2022-02-01
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2022, 23, (2), pp. 1311-1322
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/169124
dc.description.abstract	Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/TITS.2020.3024652
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.title	MagMonitor: Vehicle Speed Estimation and Vehicle Classification Through A Magnetic Sensor
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-05T01:00:46Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	2

Abstract:

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

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http://hdl.handle.net/10453/169124