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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.
1524-9050
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. (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.

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More information

Submitted date: October 2018
Accepted/In Press date: 24 January 2020
e-pub ahead of print date: 12 February 2020

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

Catalogue record

Date deposited: 17 Jan 2020 17:35
Last modified: 07 Oct 2020 02:14

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