DocumentCode :
616578
Title :
Road traffic density estimation in vehicular networks
Author :
Ruixue Mao ; Guoqiang Mao
Author_Institution :
Sch. of Electr. & Inf. Eng., Univ. of Sydney, Sydney, NSW, Australia
fYear :
2013
fDate :
7-10 April 2013
Firstpage :
4653
Lastpage :
4658
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.
Keywords :
planning; radio networks; road traffic control; scheduling; vehicle routing; vehicular ad hoc networks; intelligent road routing; road planning; road traffic control; road traffic density estimation; traffic dissemination; traffic routing; vehicular network traffic scheduling; vehicular networks; wireless communication; Accuracy; Algorithm design and analysis; Equations; Estimation; Manganese; Roads; Vehicles; Intelligent transportation systems; vehicle density estimation; vehicle-to-vehicle communication;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications and Networking Conference (WCNC), 2013 IEEE
Conference_Location :
Shanghai
ISSN :
1525-3511
Print_ISBN :
978-1-4673-5938-2
Electronic_ISBN :
1525-3511
Type :
conf
DOI :
10.1109/WCNC.2013.6555328
Filename :
6555328
Link To Document :
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