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An intelligent control system for traffic lights with simulation-based evaluation
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)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 58, p. 24-33Article in journal (Refereed) Published
Abstract [en]

This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control. It provides a convenient and economic approach to improve existing traffic light infrastructure. The control system is programmed on an intermediate hardware device capable of receiving messages from signal controller hardware as well as overriding traffic light indications during real-time operations. Signal control and optimization toolboxes are integrated into the embedded software in the FITS hardware device. A fuzzy logic based control has been implemented in FITS. In order to evaluate the effects of FITS system, this study attempts to develop a computational framework to evaluate FITS system using microscopic traffic simulation. A case study is carried out, comparing different commonly used signal control strategies with the FITS control approach. The simulation results show that the control system has the potential to improve traffic mobility, compared to all of the tested signal control strategies, due to its ability in generating flexible phase structures and making intelligent timing decisions. In addition, the effects of detector malfunction are also investigated in this study. The experiment results show that FITS exhibits superior performance than several other controllers when a few detectors are out-of-order due to its self-diagnostics feature.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 58, p. 24-33
Keywords [en]
Adaptive traffic signal control, Embedded system, Fuzzy control, Real-time traffic simulation, Adaptive control systems, Computation theory, Control systems, Controllers, Embedded systems, Fuzzy logic, Hardware, Intelligent control, Reconfigurable hardware, Street traffic control, Computational framework, Fuzzy logic based control, Intelligent traffics, Microscopic traffic simulation, Real time traffics, Real-time operation, Signal control strategy, Traffic signals
National Category
Civil Engineering Computer and Information Sciences Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-227705DOI: 10.1016/j.conengprac.2016.09.009ISI: 000390073900003Scopus ID: 2-s2.0-84990875234OAI: oai:DiVA.org:kth-227705DiVA, id: diva2:1205227
Funder
J. Gust. Richert stiftelseTrenOp, Transport Research Environment with Novel Perspectives
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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