Assessing the performance of a hybrid max-weight traffic signal control algorithm in the presence of noisy queue information: An evaluation of the environmental impacts

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Intelligent Transport Systems Pub Date : 2024-10-02 DOI:10.1049/itr2.12571
Muwahida Liaquat, Shaghayegh Vosough, Claudio Roncoli, Themistoklis Charalambous
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Abstract

Max-weight (or max-pressure) is a popular traffic signal control algorithm that has been demonstrated to be capable of optimising network-level throughput. It is based on queue size measurements in the roads approaching an intersection. However, the inability of typical sensors to accurately measure the queue size results in noisy queue measurements, which may affect the controller's performance. A possible solution is to utilise the noisy max-weight algorithm to achieve a throughput optimal solution; however, its application may lead to decreased controller performance. This article investigates two variants of max-weight controllers, namely, acyclic and cyclic max-weight (with and without noisy queue information) in simulated scenarios, by examining their impact on the throughput and environment. A detailed study of multiple pollutants, fuel consumption, and traffic conditions, which are proxied by a total social cost function, is presented to show the pros and cons of each controller. Simulation experiments, conducted for the Kamppi area in central Helsinki, Finland, show that the acyclic max-weight controller outperforms a fixed time controller, particularly in avoiding congestion and reducing emissions in the network, while the cyclic max-weight controller gives the best performance to accommodate maximum vehicles flowing in the network. The complementary positive characteristics motivated the authors to propose a new controller, herein called the hybrid max-weight, which integrates the characteristics of both acyclic and cyclic max-weight algorithms for providing better traffic load and performance through switching.

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评估混合式最大权重交通信号控制算法在队列信息噪声情况下的性能:环境影响评估
最大重量(或最大压力)是一种流行的交通信号控制算法,已被证明能够优化网络级吞吐量。该算法基于对接近交叉路口的道路上队列大小的测量。然而,典型的传感器无法准确测量队列大小,导致队列测量值产生噪声,从而可能影响控制器的性能。一种可能的解决方案是利用噪声最大权重算法来实现吞吐量最优解,但其应用可能会导致控制器性能下降。本文在模拟场景中研究了最大权重控制器的两种变体,即非周期性最大权重和周期性最大权重(有噪声队列信息和无噪声队列信息),考察了它们对吞吐量和环境的影响。通过对多种污染物、燃料消耗和交通状况的详细研究(以总社会成本函数为代表),展示了每种控制器的优缺点。在芬兰赫尔辛基市中心的 Kamppi 地区进行的模拟实验表明,非循环最大权重控制器优于固定时间控制器,尤其是在避免拥堵和减少网络排放方面,而循环最大权重控制器在适应网络中最大车辆流量方面表现最佳。这些互补的积极特性促使作者提出了一种新的控制器,即混合最大权重控制器,它综合了非循环最大权重算法和循环最大权重算法的特性,通过切换提供更好的交通负荷和性能。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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