Travel Time Prediction

Mudroch, Andrej

Travel Time Prediction

but.event.date	27.04.2017	cs
but.event.title	Student EEICT 2017	cs
dc.contributor.author	Mudroch, Andrej
dc.date.accessioned	2020-05-07T09:40:29Z
dc.date.available	2020-05-07T09:40:29Z
dc.date.issued	2017	cs
dc.description.abstract	This paper discusses the methods of travel time prediction based on the usage of machine learning and historical data. The developed prediction models are described as well as the data sources which were used as the input of the prediction models. Finally, the comparison of the models‘ performance is shown, providing proof the developed models have ability to outperform the widely used model based on instantaneous travel time that is not using statistical learning.	en
dc.format	text	cs
dc.format.extent	293-295	cs
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Proceedings of the 23st Conference STUDENT EEICT 2017. s. 293-295. ISBN 978-80-214-5496-5	cs
dc.identifier.isbn	978-80-214-5496-5
dc.identifier.uri	http://hdl.handle.net/11012/187111
dc.language.iso	sk	cs
dc.publisher	Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.relation.ispartof	Proceedings of the 23st Conference STUDENT EEICT 2017	en
dc.relation.uri	http://www.feec.vutbr.cz/EEICT/	cs
dc.rights	© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.rights.access	openAccess	en
dc.subject	travel time	en
dc.subject	prediction	en
dc.subject	regression	en
dc.title	Travel Time Prediction	en
dc.type.driver	conferenceObject	en
dc.type.status	Peer-reviewed	en
dc.type.version	publishedVersion	en
eprints.affiliatedInstitution.department	Fakulta elektrotechniky a komunikačních technologií	cs

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