NDLI: Data fusion algorithms for density reconstruction in road transportation networks

Content Provider	IEEE Xplore Digital Library
Author	Lovisari, Enrico Canudas de Wit, Carlos Kibangou, Alain Y.
Copyright Year	2015
Description	Author affiliation: Univ. Grenoble Alpes, Gipsa-Lab, France (Lovisari, Enrico; Canudas de Wit, Carlos; Kibangou, Alain Y.)
Abstract	This paper addresses the problem of density reconstruction in traffic networks with heterogeneous information sources. The network is partitioned in cells in which vehicles flow from their origin to their destination. The state of the network is represented by the densities of vehicles in each cell. Density estimation is of crucial importance in future Intelligent Transportation Systems for monitoring, control, and navigation purposes. However, deploying fixed sensors for this purpose can be very expensive. Therefore, most of fixed sensors networks are rather sparse. On the contrary, recent technologies have enormously increased the availability of relatively inexpensive Floating Car Data. A data fusion algorithm is then proposed to incorporate the two sources of information into a single observer of density of vehicles. The efficiency of the proposed algorithm is shown in a real scenario using data from the Grenoble Traffic Lab fixed sensor network and INRIX Floating Car Data on the Rocade Sud in Grenoble.
Starting Page	2804
Ending Page	2809
File Size	820363
Page Count	6
File Format	PDF
e-ISBN	9781479978861
DOI	10.1109/CDC.2015.7402641
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-15
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Sensors Roads Density measurement Velocity measurement Time measurement Observers Floating Car Data Road Transportation systems Dynamical flow network Density reconstruction
Content Type	Text
Resource Type	Article

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