Extending driver's horizon through comprehensive incident detection in vehicular networks

Chatzigiannakis, V; Grammatikou, M; Papavassiliou, S

dc.contributor.author	Chatzigiannakis, V	en
dc.contributor.author	Grammatikou, M	en
dc.contributor.author	Papavassiliou, S	en
dc.date.accessioned	2014-03-01T01:26:21Z
dc.date.available	2014-03-01T01:26:21Z
dc.date.issued	2007	en
dc.identifier.issn	0018-9545	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18024
dc.subject	Principal component analysis (PCA)	en
dc.subject	Probability theory	en
dc.subject	Road traffic	en
dc.subject	Road traffic incident detection	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Telecommunications	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Highway accidents	en
dc.subject.other	Principal component analysis	en
dc.subject.other	Probability	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Road traffic incident detection	en
dc.subject.other	Vehicular networks	en
dc.subject.other	Highway traffic control	en
dc.title	Extending driver's horizon through comprehensive incident detection in vehicular networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TVT.2007.906410	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TVT.2007.906410	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this paper, based on principal component analysis (PCA), a comprehensive and efficient incident detection approach that uses probabilistic network and processing methodologies to exploit spatial and temporal correlations and dependencies in vehicular networks, and therefore derive a reliable picture of the driving context, is proposed. The proposed approach provides an integrated way of effectively processing and organizing accumulated spatiotemporal information from a variety of different locations, vehicles, and sources and integrates it into a comprehensive outcome. The use of a PCA-based approach aims at reducing the dimensionality of the data set in which there is a large number of interrelated variables while retaining as much as possible of the variation present in the data set. The operational effectiveness of our proposed incident detection methodology is evaluated via modeling and simulation under different scenarios that represent a wide area of incidents, which range from accident occurrences to alterations in traffic patterns.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Vehicular Technology	en
dc.identifier.doi	10.1109/TVT.2007.906410	en
dc.identifier.isi	ISI:000251192000003	en
dc.identifier.volume	56	en
dc.identifier.issue	6 I	en
dc.identifier.spage	3256	en
dc.identifier.epage	3265	en