NDLI: A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

Content Provider	IEEE Xplore Digital Library
Author	Canaud, M. Mihaylova, L. Faouzi, N.E. Billot, R. Sau, J.
Copyright Year	2012
Description	Author affiliation: Université de Lyon, F-69000, Lyon, France IFSTTAR, LICIT, F-69500, Bron, ENTPE, LICIT, F-69518, Vaulx en Velin (Canaud, M.; Faouzi, N.E.; Billot, R.; Sau, J.) \|\| School of Computing and Communications, InfoLab21, South Drive, Lancaster University, Lancaster LA1 4WA, UK (Mihaylova, L.)
Abstract	Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both in the measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially the impact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied in traffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.
Starting Page	31
Ending Page	36
File Size	314624
Page Count	6
File Format	PDF
ISBN	9781467330107
e-ISBN	9781467330114
DOI	10.1109/SDF.2012.6327904
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-04
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement uncertainty Vectors Mathematical model Clutter State estimation Equations
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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