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.

中文翻译：

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通过联网车辆增强城市道路网的交通信号控制系统


城市地区交通量的增加给网络延迟和容量优化带来了挑战。然而，在智能交通系统中引入联网车辆为改善城市地区的交通流量和减少延误提供了独特的机会。本文提出了一种称为多模式自适应交通信号（MATS）的新型交通信号控制算法，该算法将联网车辆的位置信息与网络中现有感应环路和信号配时计划获得的数据相结合，以在城市十字路口执行分散的交通信号控制。 MATS 算法能够适应联网车辆数量较少的场景，在该领域，现有的联网环境交通信号控制策略受到限制。此外，还提出了一个用于测试基于伯明翰市（英国）大型城市道路网的联网交通信号控制器的框架。将 MATS 算法与单个交叉点上的 MOVA 进行比较，并在所提出的测试框架上校准 TRANSYT 计划。结果表明，对于 0-100% 联网车辆的网络，MATS 算法比 MOVA 的平均延迟减少高达 28%，比 TRANSYT 的平均延迟和平均停靠次数分别减少高达 96% 和 33%在场。 MATS 算法在非理想通信信道条件下以及道路网络上存在大量交通需求时也表现出鲁棒性。