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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation
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: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 114, p. 348-360Article in journal (Refereed) Published
Abstract [en]

Traffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this work, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and in application it is further enhanced by a sampling approach for initial population and utilizations of adaptive crossover and mutation probabilities. The final algorithm shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 114, p. 348-360
Keywords [en]
Simulation-based optimization, Archived genetic algorithm, Road traffic controls, Traffic light control, Object-oriented software framework
National Category
Computer Sciences Transport Systems and Logistics Software Engineering
Research subject
Transport Science; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-227702DOI: 10.1016/j.advengsoft.2017.08.005ISI: 000415593800027Scopus ID: 2-s2.0-85034032493OAI: oai:DiVA.org:kth-227702DiVA, id: diva2:1205218
Funder
Swedish Transport Administration, Dnr TRV 2013/16116
Note

QC 20180514

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-14Bibliographically 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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