Weigh-in-motion using machine learning and telematics

Kirushanth, S.; Kabaso, B.

doi:10.1109/TAFGEN.2018.8580463

dc.contributor.author	Kirushanth, S.
dc.contributor.author	Kabaso, B.
dc.date.accessioned	2025-09-09T07:37:53Z
dc.date.available	2025-09-09T07:37:53Z
dc.date.issued	2018-07-24
dc.identifier.citation	S. Kirushnath and B. Kabaso, "Weigh-in-motion Using Machine Learning and Telematics," 2018 2nd International Conference on Telematics and Future Generation Networks (TAFGEN), Kuching, Malaysia, 2018, pp. 115-120, doi: 10.1109/TAFGEN.2018.8580463.	en_US
dc.identifier.uri	http://drr.vau.ac.lk/handle/123456789/1213
dc.description.abstract	Driving overloaded vehicle causes road infrastructural damages, accidents, air pollution by excessive fuel consumption, and unusual expenses. Measuring the gross weight of a vehicle on a particular road segment without interrupting the traffic flow is a problem worth researching, and its solutions have several economic benefits. This paper proposes an alternative way of finding an overloaded vehicle in motion, by introducing a novel approach in inferring the weight of a vehicle on a road segment using Telematics data and Machine Learning.	en_US
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.source.uri	https://ieeexplore.ieee.org/abstract/document/8580463	en_US
dc.subject	Telematics	en_US
dc.subject	WIM	en_US
dc.subject	Machine Learning	en_US
dc.subject	ECU	en_US
dc.subject	Sensors	en_US
dc.subject	Engines	en_US
dc.subject	Tires	en_US
dc.subject	Artificial neural networks	en_US
dc.title	Weigh-in-motion using machine learning and telematics	en_US
dc.type	Conference full paper	en_US
dc.identifier.doi	10.1109/TAFGEN.2018.8580463	en_US
dc.identifier.proceedings	2018 2nd International Conference on Telematics and Future Generation Networks (TAFGEN)	en_US