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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.
Original language | English |
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Pages (from-to) | 2255-2272 |
Number of pages | 18 |
Journal | IET Intelligent Transport Systems |
Volume | 18 |
Issue number | 11 |
Early online date | 2 Oct 2024 |
DOIs | |
Publication status | Published - Nov 2024 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Distributed control
- Environmental impact assessment
- Max-weight control
- Noisy queue estimation
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Dive into the research topics of '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'. Together they form a unique fingerprint.Projects
- 2 Active
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AlforLEssAuto: Artificial Intelligence for Urban Low-Emission Autonomous Traffic (AIforLEssAuto)
Roncoli, C. (Principal investigator), Vosough, S. (Project Member), Yang, Y. (Project Member), Zhou, Z. (Project Member) & Haris, M. (Project Member)
EU The Recovery and Resilience Facility (RRF)
01/01/2022 → 31/12/2024
Project: Academy of Finland: Other research funding
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FinEst Twins: FinEst Twins
Nieminen, M. (Principal investigator)
01/12/2019 → 30/11/2026
Project: EU: Framework programmes funding