The University of Southampton
University of Southampton Institutional Repository

Augmenting traffic signal control systems for urban road networks with connected vehicles

Augmenting traffic signal control systems for urban road networks with connected vehicles
Augmenting traffic signal control systems for urban road networks with connected vehicles
The increase in traffic volumes in urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles in intelligent transport systems presents unique opportunities for improving traffic flow and reducing delays in urban areas. This paper proposes a novel traffic signal control algorithm called Multi-mode Adaptive Traffic Signals (MATS) which combines position information from connected vehicles with data obtained from existing inductive loops and signal timing plans in the network to perform decentralised traffic signal control at urban intersections. The MATS algorithm is capable of adapting to scenarios with low numbers of connected vehicles, an area where existing traffic signal control strategies for connected environments are limited. Additionally, a framework for testing connected traffic signal controllers based on a large urban road network in the city of Birmingham (UK) is presented. The MATS algorithm is compared with MOVA on a single intersection, and a calibrated TRANSYT plan on the proposed testing framework. The results show that the MATS algorithm offers reductions in mean delay up to 28% over MOVA, and reductions in mean delay and mean numbers of stops of up to 96% and 33% respectively over TRANSYT, for networks with 0-100% connected vehicle presence. The MATS algorithm is also shown to be robust under non-ideal communication channel conditions, and when heavy traffic demand prevails on the road network.
Intelligent transport systems, V2I, adaptive signal control, communication systems, connected vehicles, traffic signal control
1524-9050
1728-1740
Rafter, Craig Benjamin
8f56b72d-8984-47e4-ae2a-f38a68fbad14
Anvari, Bani
f94e2ccb-1d88-4980-8d29-f4281995d072
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Cherrett, Thomas
e5929951-e97c-4720-96a8-3e586f2d5f95
Rafter, Craig Benjamin
8f56b72d-8984-47e4-ae2a-f38a68fbad14
Anvari, Bani
f94e2ccb-1d88-4980-8d29-f4281995d072
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Cherrett, Thomas
e5929951-e97c-4720-96a8-3e586f2d5f95

Rafter, Craig Benjamin, Anvari, Bani, Box, Simon and Cherrett, Thomas (2020) Augmenting traffic signal control systems for urban road networks with connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 21 (4), 1728-1740, [8995570]. (doi:10.1109/TITS.2020.2971540).

Record type: Article

Abstract

The increase in traffic volumes in urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles in intelligent transport systems presents unique opportunities for improving traffic flow and reducing delays in urban areas. This paper proposes a novel traffic signal control algorithm called Multi-mode Adaptive Traffic Signals (MATS) which combines position information from connected vehicles with data obtained from existing inductive loops and signal timing plans in the network to perform decentralised traffic signal control at urban intersections. The MATS algorithm is capable of adapting to scenarios with low numbers of connected vehicles, an area where existing traffic signal control strategies for connected environments are limited. Additionally, a framework for testing connected traffic signal controllers based on a large urban road network in the city of Birmingham (UK) is presented. The MATS algorithm is compared with MOVA on a single intersection, and a calibrated TRANSYT plan on the proposed testing framework. The results show that the MATS algorithm offers reductions in mean delay up to 28% over MOVA, and reductions in mean delay and mean numbers of stops of up to 96% and 33% respectively over TRANSYT, for networks with 0-100% connected vehicle presence. The MATS algorithm is also shown to be robust under non-ideal communication channel conditions, and when heavy traffic demand prevails on the road network.

Text
Rafter_et_al_IEEE_ITS_MATS - Author's Original
Download (1MB)
Text
cbrafter_ieeeits_augmenting_traffic_signals_oct18
Restricted to Repository staff only
Request a copy

More information

Submitted date: October 2018
Accepted/In Press date: 24 January 2020
e-pub ahead of print date: 12 February 2020
Published date: April 2020
Keywords: Intelligent transport systems, V2I, adaptive signal control, communication systems, connected vehicles, traffic signal control

Identifiers

Local EPrints ID: 437128
URI: http://eprints.soton.ac.uk/id/eprint/437128
ISSN: 1524-9050
PURE UUID: 1c94ebdf-4d82-419e-b2d1-e3d63c5943d4
ORCID for Craig Benjamin Rafter: ORCID iD orcid.org/0000-0003-3411-114X
ORCID for Bani Anvari: ORCID iD orcid.org/0000-0001-7916-7636
ORCID for Thomas Cherrett: ORCID iD orcid.org/0000-0003-0394-5459

Catalogue record

Date deposited: 17 Jan 2020 17:35
Last modified: 16 Mar 2024 02:48

Export record

Altmetrics

Contributors

Author: Craig Benjamin Rafter ORCID iD
Author: Bani Anvari ORCID iD
Author: Simon Box
Author: Thomas Cherrett ORCID iD

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.

×