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A multi-objective agent-based approach for road traffic controls: application for adaptive traffic signal systems
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. (System Simulation & Control (S2CLab))ORCID iD: 0000-0002-1375-9054
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. (System Simulation & Control (S2CLab))
2017 (English)Manuscript (preprint) (Other academic)
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 multi-objective Markov decision process is applied to model agent operations, allowing agents to form a decision in the context of multiple policy goals. The problem is reformulated by a constrained Markov decision process (CMDP) to enhance the computational efficiency. In the study, the policy goal with the highest priority becomes the optimization objective, but the other objectives are transferred as constraints for optimization. A reinforcement learning based approach is developed with different function approximation methods used to enhance the control algorithm. For implementation of multi-objective control, a threshold lexicographic ordering method is introduced and integrated with the learning algorithm. While the multi-objective intelligent control method could be potentially applied for different road traffic controls, this paper demonstrates a case study on traffic signal control in a road network in Stockholm. Intersections are modeled as agents that can make intelligent timing decisions according to the detected traffic states and update their knowledge from system feedback. The evaluation results show the benefits offered by the control approach especially when multiple policy requirements are introduced.

Place, publisher, year, edition, pages
2017. Vol. 114, p. 348-360
Keywords [en]
Agent-based system, intelligent control, multi-objective reinforcement learning, traffic signal control
National Category
Computer Sciences Transport Systems and Logistics Software Engineering
Research subject
Transport Science; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-227712OAI: oai:DiVA.org:kth-227712DiVA, id: diva2:1205233
Note

QC 20180514

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-25Bibliographically approved
In thesis
1. Advance Traffic Signal Control Systems with Emerging Technologies
Open this publication in new window or tab >>Advance Traffic Signal Control Systems with Emerging Technologies
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Nowadays, traffic congestion poses critical problems including the undermined mobility and sustainability efficiencies. Mitigating traffic congestions in urban areas is a crucial task for both research and in practice. With decades of experience in road traffic controls, there is still room for improving traffic control measures; especially with the emerging technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and Big Data. The focus of this thesis lies in the development and implementation of enhanced traffic signal control systems, one of the most ubiquitous and challenging traffic control measures.

This thesis makes the following major contributions. Firstly, a simulation-based optimization framework is proposed, which is inherently general in which various signal control types, and different simulation models and optimization methods can be integrated. Requiring heavy computing resources is a common issue of simulation-based optimization approaches, which is addressed by an advanced genetic algorithm and parallel traffic simulation in this study.

The second contribution is an investigation of an intelligent local control system. The local signal control operation is formulated as a sequential decision-making process where each controller or control component is modeled as an intelligent agent. The agents make decisions based on traffic conditions and the deployed road infrastructure, as well as the implemented control scheme. A non-parametric state estimation method and an adaptive control scheme by reinforcement learning (RL) are introduced to facilitate such an intelligent system.

The local intelligence is expanded to an arterial road using a decentralized design, which is enabled by a hierarchical framework. Then, a network of signalized intersections is operated under the cooperation of agents at different levels of hierarchy. An agent at a lower level is instructed by the agent at the next higher level toward a common operational goal. Agents at the same level can communicate with their neighbors and perform collective behaviors.

Additionally, a multi-objective RL approach is in use to handle the potential conflict between agents at different hierarchical levels. Simulation experiments have been carried out, and the results verify the capabilities of the proposed methodologies in traffic signal control applications. Furthermore, this thesis demonstrates an opportunity to employ the systems in practice when the system is programmed on an intermediate hardware device. Such a device can receive streaming detection data from signal controller hardware or the simulation environment and override the controlled traffic lights in real time.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018
Series
TRITA-ABE-DLT ; 189
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-227713 (URN)978-91-7729-758-1 (ISBN)
Public defence
2018-06-12, F3, Lindstedtsvägen 26, Stockholm, 14:00 (English)
Opponent
Supervisors
Available from: 2018-05-14 Created: 2018-05-11 Last updated: 2018-05-14Bibliographically approved

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Jin, JunchenMa, Xiaoliang

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