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A group-based traffic signal control with adaptive learning ability
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. iTekn Solutions, Sweden. (System Simulation & Control (S2CLab))
2017 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 65, p. 282-293Article in journal (Refereed) Published
Abstract [en]

Group-based control is an advanced traffic signal strategy capable of dynamically generating phase sequences at intersections. Combined with the phasing scheme, vehicle actuated timing is often adopted to respond to the detected traffic. However, the parameters of a signal controller are often predetermined in practice, and the control performance may suffer from deterioration when dealing with highly fluctuating traffic demand. This study proposes a group-based signal control approach capable of making decisions based on its understanding of traffic conditions at the intersection level. In particular, the control problem is formulated using a framework of stochastic optimal control for multi-agent system in which each signal group is modeled as an intelligent agent. The agents learn how to react to traffic environment and make optimal timing decisions according to the perceived system states. Reinforcement learning, enhanced by multiple-step backups, is employed as the kernel of the intelligent control algorithm, where each agent updates its knowledge on-line based on a sequence of states during the process. In addition, the proposed system is designated to be compatible with the prevailing signal system. A case study was carried out in a simulation environment to compare the proposed control approach with a benchmark controller used in practice, group-based vehicle actuated (GBVA) controller, whose parameters were off-line optimized using a genetic algorithm. Simulation results show that the proposed adaptive group-based control system outperforms the optimized GBVA control system mainly because of its real-time adaptive learning capacity in response to the changes in traffic demand.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 65, p. 282-293
National Category
Transport Systems and Logistics Control Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-227708DOI: 10.1016/j.engappai.2017.07.022ISI: 000413388100025Scopus ID: 2-s2.0-85028311036OAI: oai:DiVA.org:kth-227708DiVA, id: diva2:1205228
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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