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A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. Enjoyor Co Ltd, Hangzhou 310030, Zhejiang, Peoples R China..ORCID iD: 0000-0002-1375-9054
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.ORCID iD: 0000-0001-5526-4511
2019 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 20, no 10, p. 3900-3912Article in journal (Refereed) Published
Abstract [en]

Agent-based approaches have gained popularity in engineering applications, but its potential for advanced traffic controls has not been sufficiently explored. This paper presents a multi-agent framework that models traffic control instruments and their interactions with road traffic. A constrained Markov decision process (CMDP) model is used to represent agent decision making in the context of multi-objective policy goals, where the policy goal with the highest priority becomes the single optimization objective and the other goals are transformed as constraints. A reinforcement learning-based computational framework is developed for control applications. To implement the multi-objective decision model, a threshold lexicographic ordering method is introduced and integrated with the learning-based algorithm. Moreover, a two-stage hybrid framework is established to improve the learning efficiency of the model. While the proposed approach is potentially applicable for different road traffic operations, this paper applies the framework for traffic signal control in a network of Stockholm based on traffic simulation. The computational results show that the proposed control approach can handle a complex case of multiple policy requirements. Meanwhile, the agent-based intelligent control has shown superior performance when compared to other optimized signal control methods.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 20, no 10, p. 3900-3912
Keywords [en]
Agent-based system, intelligent control, multi-objective reinforcement learning, hybrid learning model, traffic signal control
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-262959DOI: 10.1109/TITS.2019.2906260ISI: 000489747100028Scopus ID: 2-s2.0-85077516849OAI: oai:DiVA.org:kth-262959DiVA, id: diva2:1375709
Note

QC 20191205

Available from: 2019-12-05 Created: 2019-12-05 Last updated: 2020-03-09Bibliographically approved

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Jin, JunchenMa, Xiaoliang

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