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

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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Identifiers

ORCID for Craig Benjamin Rafter: orcid.org/0000-0003-3411-114X

ORCID for Bani Anvari: orcid.org/0000-0001-7916-7636

ORCID for Thomas Cherrett: orcid.org/0000-0003-0394-5459

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