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A multi-criteria intelligent control for traffic lights using reinforcement learning
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.ORCID iD: 0000-0002-1375-9054
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
2018 (English)In: Advanced Concepts, Methodologies and Technologies for Transportation and Logistics, Springer Verlag , 2018, p. 438-451Chapter in book (Refereed)
Abstract [en]

Traffic signal control plays a crucial role in traffic management and operation practices. In the past decade, adaptive signal control systems, capable of adjusting control schemes in response to traffic patterns, have shown the abilities to improve traffic mobility. On the other hand, the negative impacts on environments by increased vehicles attract increased attentions from traffic stakeholders and the general public. Most of the prevalent adaptive signal control systems do not address energy and environmental issues. The present paper proposes an adaptive signal control system capable of taking multi-criteria strategies into account. A general multi-agent framework is introduced for modeling signal control operations. The behavior of each cognitive agent is modeled by a Constrained Markov Decision Process (CMDP). Reinforcement learning algorithms are applied to solve the MDP problem. As a result, the signal controller makes intelligent timing decisions according to a pre-defined policy goal. A case study is carried out for the stage-based control scheme to investigate the effectiveness of the adaptive signal control system from two perspectives, traffic mobility and energy efficiency. The control approach can be further applied to a large network in a decentralized manner. 

Place, publisher, year, edition, pages
Springer Verlag , 2018. p. 438-451
Keywords [en]
Adaptive traffic light control, Intelligent timing decision, Multi-criteria strategies, Reinforcement Learning, Adaptive control systems, Control systems, Education, Energy efficiency, Intelligent agents, Learning algorithms, Markov processes, Multi agent systems, Street traffic control, Adaptive signal control systems, Adaptive traffic lights, Constrained Markov decision process, Environmental issues, Multi-criteria, Multiagent framework, Timing decisions, Traffic signal control, Traffic signals
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-216808DOI: 10.1007/978-3-319-57105-8_22Scopus ID: 2-s2.0-85022210559OAI: oai:DiVA.org:kth-216808DiVA, id: diva2:1162775
Note

Export Date: 24 October 2017; Book Chapter; Correspondence Address: Jin, J.; System Simulation and Control, Department of Transport Science, KTH Royal Institute of Technology, Teknikringen 10, Sweden; email: junchen@kth.se. QC 20171205

Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-01-13Bibliographically approved

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Jin, JunchenMa, Xiaoliang

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