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dc.contributor.author Won Myounggyu ko
dc.contributor.author Sahu Sayan ko
dc.contributor.author Park, Kyung-Joon ko
dc.date.accessioned 2021-01-29T07:23:37Z -
dc.date.available 2021-01-29T07:23:37Z -
dc.date.created 2020-06-05 -
dc.date.issued 2019-11-07 -
dc.identifier.citation 16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019, pp.476 - 484 -
dc.identifier.isbn 9781728146010 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12882 -
dc.description.abstract A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS). It is an essential tool for traffic analysis and planning. One of the biggest challenges is, however, the high cost especially in covering the huge rural road network. In this paper, we propose to address the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a low-cost, portable, and non-intrusive solution that is built only with two WiFi transceivers. It exploits the unique WiFi Channel State Information (CSI) of passing vehicles to perform detection and classification of vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a machine learning technique to classify vehicles into five different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and large trucks. A large amount of CSI data and ground-truth video data are collected over a month period from a real-world two-lane rural roadway to validate the effectiveness of DeepWiTraffic. The results validate that DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4% and the average classification accuracy of 91.1% in comparison with state-of-the-art non-intrusive TMSs. © 2019 IEEE. -
dc.language English -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title DeepWiTraffic: Low cost WiFi-based traffic monitoring system using deep learning -
dc.type Conference -
dc.identifier.doi 10.1109/MASS.2019.00062 -
dc.identifier.scopusid 2-s2.0-85085019284 -
dc.type.local Article(Overseas) -
dc.type.rims CONF -
dc.description.journalClass 1 -
dc.contributor.localauthor Park, Kyung-Joon -
dc.contributor.nonIdAuthor Won Myounggyu -
dc.contributor.nonIdAuthor Sahu Sayan -
dc.identifier.citationStartPage 476 -
dc.identifier.citationEndPage 484 -
dc.identifier.citationTitle 16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019 -
dc.identifier.conferencecountry US -
dc.identifier.conferencelocation Monterey -

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