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Advance Traffic Signal Control Systems with Emerging Technologies
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.ORCID iD: 0000-0002-1375-9054
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: urn:nbn:se:kth:diva-227713ISBN: 978-91-7729-758-1 (print)OAI: oai:DiVA.org:kth-227713DiVA, id: diva2:1205236
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
List of papers
1. Multi-criteria analysis of optimal signal plans using microscopic traffic models
Open this publication in new window or tab >>Multi-criteria analysis of optimal signal plans using microscopic traffic models
2014 (English)In: Transportation Research Part D: Transport and Environment, ISSN 1361-9209, E-ISSN 1879-2340, Vol. 32, p. 1-14Article in journal (Refereed) Published
Abstract [en]

Increasing concerns on environment and natural resources, coupled with increasing demand for transport, put lots of pressure for improved efficiency and performance on transport systems worldwide. New technology nowadays enables fast innovation in transport, but it is the policy for deployment and operation with a systems perspective that often determines success. Smart traffic management has played important roles for continuous development of traffic systems especially in urban areas. There is, however, still lack of effort in current traffic management and planning practice prioritizing policy goals in environment and energy. This paper presents an application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability. Microscopic traffic simulator is integrated with micro-scale emission model for estimation of emissions and fuel consumption at high resolution. A stochastic optimization engine is implemented to facilitate optimal signal planning for different policy goals, including delay, stop-and-goes, fuel economy etc. In order to enhance the validity of the modeling framework, both traffic and emission models are fine-tuned using data collected in a Chinese city. In addition, two microscopic traffic models are applied, and lead to consistent results for signal optimization. Two control schemes, fixed time and vehicle actuated, are optimized while multiple performance indexes are analyzed and compared for corresponding objectives. Solutions, representing compromise between different policies, are also obtained in the case study by optimizing an integrated performance index.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Optimal signal planning, Stochastic optimization, Environmental impacts, Energy efficiency, Microscopic traffic simulation, Emission model
National Category
Environmental Sciences
Identifiers
urn:nbn:se:kth:diva-157219 (URN)10.1016/j.trd.2014.06.013 (DOI)000344212600001 ()2-s2.0-84904970956 (Scopus ID)
Funder
Swedish Research Council, 348-2007-6398
Note

QC 20141209

Available from: 2014-12-09 Created: 2014-12-08 Last updated: 2018-05-11Bibliographically approved
2. A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation
Open this publication in new window or tab >>A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation
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
Keywords
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:nbn:se:kth:diva-227702 (URN)10.1016/j.advengsoft.2017.08.005 (DOI)000415593800027 ()2-s2.0-85034032493 (Scopus ID)
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
3. A group-based traffic signal control with adaptive learning ability
Open this publication in new window or tab >>A group-based traffic signal control with adaptive learning ability
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
National Category
Transport Systems and Logistics Control Engineering Computer Sciences
Identifiers
urn:nbn:se:kth:diva-227708 (URN)10.1016/j.engappai.2017.07.022 (DOI)000413388100025 ()2-s2.0-85028311036 (Scopus ID)
Note

QC 20180514

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-25Bibliographically approved
4. A non-parametric Bayesian framework for traffic-state estimation at signalized intersections
Open this publication in new window or tab >>A non-parametric Bayesian framework for traffic-state estimation at signalized intersections
2017 (English)Manuscript (preprint) (Other academic)
Abstract [en]

An accurate and practical traffic-state estimation (TSE) method for signalized intersections plays an important role for real-time operations to facilitate efficient traffic management. This paper presents a generalized modeling framework for estimating traffic states at signalized intersections. The framework is non-parametric and data-driven, without the requirement on explicit models of traffic. Additionally, in principle, any type of data source together with any type of signal controller can be incorporated with the proposed framework. The Bayesian filter (BF) approach is the core of the framework and introduces a recursive state estimation process. The required transition and measurement models of the BFs are trained using Gaussian process (GP) regression models with respect to a set of historical data. A Gaussian process model uses kernel functions to describe the proximity among data points, and the hyper-parameters adopted in the GP model are optimized according to the training data. In addition to the detailed derivation of the integration of BFs and GP regression models, an algorithm based on the extended Kalman filter is presented for real-time traffic estimation. The effectiveness of the proposed framework is demonstrated through several numerical experiments using data generated in microscopic traffic simulations. Both fixed-location data (i.e., loop detector) and mobile data (i.e., connected vehicle) are examined with the framework. As a result, the method performs well for the tested traffic conditions. In particular, the estimator provides a competitive estimation accuracy merely using the position information of a small portion of vehicles at the intersection. The approach is suitable for a short-term estimation requirement, which is normally a challenging task in traffic control and operations.

Keywords
Traffic state estimation, data-driven model, non-parametric framework, Bayesian filters, Gaussian process regression
National Category
Computer Sciences Transport Systems and Logistics Software Engineering
Research subject
Transport Science; Computer Science
Identifiers
urn:nbn:se:kth:diva-227711 (URN)
Note

QC 20180514

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-25Bibliographically approved
5. Hierarchical multi-agent control of traffic lights based on collective learning
Open this publication in new window or tab >>Hierarchical multi-agent control of traffic lights based on collective learning
2018 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 68, p. 236-248Article in journal (Refereed) Published
Abstract [en]

Increasing traffic congestion poses significant challenges for urban planning and management in metropolitan areas around the world. One way to tackle the problem is to resort to the emerging technologies in artificial intelligence. Traffic light control is one of the most traditional and important instruments for urban traffic management. The present study proposes a traffic light control system enabled by a hierarchical multi-agent modeling framework in a decentralized manner. In the framework, a traffic network is decomposed into regions represented by region agents. Each region consists of intersections, modeled by intersection agents who coordinate with neighboring intersection agents through communication. For each intersection, a collection of turning movement agents operate individually and implement optimal actions according to local control policies. By employing a reinforcement learning algorithm for each turning movement agent, the intersection controllers are enabled with the capability to make their timing decisions in a complex and dynamic environment. In addition, the traffic light control operates with an advanced phase composition process dynamically combining compatible turning movements. Moreover, the collective operations performed by the agents in a road network are further coordinated by varying priority settings for relevant turning movements. A case study was carried out by simulations to evaluate the performance of the proposed control system while comparing it with an optimized vehicle-actuated control system. The results show that the proposed traffic light system, after a collective machine learning process, not only improves the local signal operations at individual intersections but also enhances the traffic performance at the regional level through coordination of specific turning movements.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Hierarchical model of traffic system, Multi-agent traffic light control, Decentralized system, Learning-based control, Collective machine learning
National Category
Other Engineering and Technologies Transport Systems and Logistics Computer and Information Sciences
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-223272 (URN)10.1016/j.engappai.2017.10.013 (DOI)000423894400021 ()2-s2.0-85034445822 (Scopus ID)
Funder
J. Gust. Richert stiftelse, 2015-00205
Note

QC 20180514

Available from: 2018-02-16 Created: 2018-02-16 Last updated: 2018-05-25Bibliographically approved
6. A multi-objective agent-based approach for road traffic controls: application for adaptive traffic signal systems
Open this publication in new window or tab >>A multi-objective agent-based approach for road traffic controls: application for adaptive traffic signal systems
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.

Keywords
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:nbn:se:kth:diva-227712 (URN)
Note

QC 20180514

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-25Bibliographically approved
7. An intelligent control system for traffic lights with simulation-based evaluation
Open this publication in new window or tab >>An intelligent control system for traffic lights with simulation-based evaluation
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
Keywords
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:nbn:se:kth:diva-227705 (URN)10.1016/j.conengprac.2016.09.009 (DOI)000390073900003 ()2-s2.0-84990875234 (Scopus ID)
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

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