A comparison of machine learning models for speed estimation

Antoniou, C; Koutsopoulos, HN

dc.contributor.author	Antoniou, C	en
dc.contributor.author	Koutsopoulos, HN	en
dc.date.accessioned	2014-03-01T02:50:17Z
dc.date.available	2014-03-01T02:50:17Z
dc.date.issued	2006	en
dc.identifier.issn	14746670	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35031
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-79961101037&partnerID=40&md5=c9be9a95f5e00a09d2c9094218c59c3f	en
dc.subject	Machine learning	en
dc.subject	Neural networks	en
dc.subject	Non-parametric regression	en
dc.subject	Road traffic	en
dc.subject.other	Alternative approach	en
dc.subject.other	Dynamic traffic assignments	en
dc.subject.other	Locally weighted regression	en
dc.subject.other	Machine learning methods	en
dc.subject.other	Machine-learning	en
dc.subject.other	Non-parametric regression	en
dc.subject.other	On-machines	en
dc.subject.other	Road traffic	en
dc.subject.other	Sensor data	en
dc.subject.other	Simulation-based	en
dc.subject.other	Speed estimation	en
dc.subject.other	Speed-density relationships	en
dc.subject.other	Support vector regressions	en
dc.subject.other	Traffic dynamics	en
dc.subject.other	Traffic flow theory	en
dc.subject.other	Traffic simulations	en
dc.subject.other	Traffic streams	en
dc.subject.other	Computer simulation	en
dc.subject.other	Estimation	en
dc.subject.other	Neural networks	en
dc.subject.other	Regression analysis	en
dc.subject.other	Stream flow	en
dc.subject.other	Traffic control	en
dc.subject.other	Learning systems	en
dc.title	A comparison of machine learning models for speed estimation	en
heal.type	conferenceItem	en
heal.publicationDate	2006	en
heal.abstract	Speed-density relationships are a classic way of modeling stationary traffic relationships. Besides offering valuable insight in traffic stream flows, such relationships are widely used in simulation-based Dynamic Traffic Assignment (DTA) systems. In this paper, alternative approaches for modeling traffic dynamics, appropriate for traffic simulation, are proposed. Their basic premise is the wide availability of sensor data. The approaches are based on machine learning methods such as locally weighted regression and support vector regression. Neural networks are also considered, as they are a well-established approach, successful in many applications. While such models may not provide as much insight into traffic flow theory, they allow for easy incorporation of additional information to speed estimation, and hence, may be more appropriate for use in DTA models, especially simulation based. In particular, in this paper, it is demonstrated (using data from a network in Irvine, CA) that the use of such machine learning methods can improve the accuracy of speed estimation. Copyright © 2006 IFAC.	en
heal.journalName	IFAC Proceedings Volumes (IFAC-PapersOnline)	en
dc.identifier.volume	11	en
dc.identifier.issue	PART 1	en
dc.identifier.spage	55	en
dc.identifier.epage	60	en