The University of Southampton
University of Southampton Institutional Repository

A methodology for traffic state estimation and signal control utilizing high wireless device penetration

A methodology for traffic state estimation and signal control utilizing high wireless device penetration
A methodology for traffic state estimation and signal control utilizing high wireless device penetration
This paper presents a methodology for fusing data from multiple sensors, including wireless devices, to make an estimation of the state of an urban traffic network. An extended Kalman filter is employed along with a state evolution model to make estimates of the state in a discretized network. Results are presented from simulation tests of signal controllers on a network with three signalized junctions. Two signal control methods are tested: SCOOT and a machine learning junction control algorithm that employs the discretized state structure described in this paper. These tests represent lower and upper performance benchmarks and present a significant difference. The tests also demonstrate a framework for the future evaluation of the proposed methodology.
Box, S.
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Snell, I.
f84d7acc-0223-4b4b-954a-2c825dbcace7
Waterson, B.
60a59616-54f7-4c31-920d-975583953286
Hamilton, Andrew
ae7c13b2-0575-4579-8290-94922544f742
Box, S., Snell, I., Waterson, B. and Hamilton, Andrew (2012) A methodology for traffic state estimation and signal control utilizing high wireless device penetration At 19th ITS World Congress, Austria. 21 - 26 Oct 2012.

Box, S., Snell, I., Waterson, B. and Hamilton, Andrew (2012) A methodology for traffic state estimation and signal control utilizing high wireless device penetration At 19th ITS World Congress, Austria. 21 - 26 Oct 2012.

Record type: Conference or Workshop Item (Paper)

Abstract

This paper presents a methodology for fusing data from multiple sensors, including wireless devices, to make an estimation of the state of an urban traffic network. An extended Kalman filter is employed along with a state evolution model to make estimates of the state in a discretized network. Results are presented from simulation tests of signal controllers on a network with three signalized junctions. Two signal control methods are tested: SCOOT and a machine learning junction control algorithm that employs the discretized state structure described in this paper. These tests represent lower and upper performance benchmarks and present a significant difference. The tests also demonstrate a framework for the future evaluation of the proposed methodology.

PDF BoxEtAlITSviennaFinal.pdf - Author's Original
Download (301kB)

More information

Published date: 23 October 2012
Venue - Dates: 19th ITS World Congress, Austria, 2012-10-21 - 2012-10-26
Organisations: Transportation Group

Identifiers

Local EPrints ID: 350156
URI: http://eprints.soton.ac.uk/id/eprint/350156
PURE UUID: 55000843-f80b-4dc5-b427-c6d4f9e20f3d
ORCID for B. Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 19 Mar 2013 14:43
Last modified: 18 Jul 2017 04:37

Export record

Contributors

Author: S. Box
Author: I. Snell
Author: B. Waterson ORCID iD
Author: Andrew Hamilton

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×