Road traffic density estimation in vehicular networks

Mao, R; Mao, G

Road traffic density estimation in vehicular networks

Mao, R Mao, G

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Publication Type:: Conference Proceeding
Citation:: IEEE Wireless Communications and Networking Conference, WCNC, 2013, pp. 4653 - 4658
Issue Date:: 2013-08-21

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Field	Value	Language
dc.contributor.author	Mao, R	en_US
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949	en_US
dc.date.issued	2013-08-21	en_US
dc.identifier.citation	IEEE Wireless Communications and Networking Conference, WCNC, 2013, pp. 4653 - 4658	en_US
dc.identifier.isbn	9781467359399	en_US
dc.identifier.issn	1525-3511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29973
dc.description.abstract	Road traffic density estimation provides important information for road planning, intelligent road routing, road traffic control, vehicular network traffic scheduling, routing and dissemination. The ever increasing number of vehicles equipped with wireless communication capabilities provide new means to estimate the road traffic density more accurately and in real time than traditionally used techniques. In this paper, we consider the problem of road traffic density estimation where each vehicle estimates its local road traffic density using some simple measurements only, i.e. the number of neighboring vehicles. A maximum likelihood estimator of the traffic density is obtained based on a rigorous analysis of the joint distribution of the number of vehicles in each hop. Analysis is also performed on the accuracy of the estimation and the amount of neighborhood information required for an accurate road traffic density estimation. Simulations are performed which validate the accuracy and the robustness of the proposed density estimation algorithm. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE Wireless Communications and Networking Conference, WCNC	en_US
dc.relation.isbasedon	10.1109/WCNC.2013.6555328	en_US
dc.title	Road traffic density estimation in vehicular networks	en_US
dc.type	Conference Proceeding
utslib.for	080501 Distributed and Grid Systems	en_US
utslib.for	100503 Computer Communications Networks	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Shanghai	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Road traffic density estimation provides important information for road planning, intelligent road routing, road traffic control, vehicular network traffic scheduling, routing and dissemination. The ever increasing number of vehicles equipped with wireless communication capabilities provide new means to estimate the road traffic density more accurately and in real time than traditionally used techniques. In this paper, we consider the problem of road traffic density estimation where each vehicle estimates its local road traffic density using some simple measurements only, i.e. the number of neighboring vehicles. A maximum likelihood estimator of the traffic density is obtained based on a rigorous analysis of the joint distribution of the number of vehicles in each hop. Analysis is also performed on the accuracy of the estimation and the amount of neighborhood information required for an accurate road traffic density estimation. Simulations are performed which validate the accuracy and the robustness of the proposed density estimation algorithm. © 2013 IEEE.

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http://hdl.handle.net/10453/29973