A Test Bed for Multi-Agent Control Systems in Road Traffic Management

Katwijk, R.T. van; Koningsbruggen, Paul van; Schutter, Bart De; Hellendoorn, J.

doi:10.1007/3-7643-7363-6_8

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…In van Katwijk et al . (2005), a testbed for traffic management is presented, which intends to manage different levels of complexity, a diversity of policy goals, and different forms of traffic problems. Their testbed aims at rapid development of multiagent-based management and control, consisting of an interaction model, intelligence models, and a world model.…”

Section: Agent-based Control and Managementmentioning

confidence: 99%

A review on agent-based technology for traffic and transportation

Bazzan

Klügl

2013

The Knowledge Engineering Review

241

117

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In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges.

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Section: Agent-based Control and Managementmentioning

confidence: 99%

A review on agent-based technology for traffic and transportation

Bazzan

Klügl

2013

The Knowledge Engineering Review

241

117

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“…The first of these levels is discussed in several papers, e.g. the platform called Multi-Agent Environment for Constructing Cooperative Applications-MECCA/UTS- [36], as well as in [57,67,84]. The other two levels are discussed next.…”

Section: Conceptual and Organizational Aspects In Traffic Engineeringmentioning

confidence: 99%

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Bazzan

2008

Auton Agent Multi-Agent Syst

199

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The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the "agentification" of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them.

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“…There is a wide variety of types of signalized traffic control ( 3 , 4 , 6 ). Signalized traffic control methods can be divided into two general categories: fixed-time control and traffic-responsive control.…”

Section: State Of the Artmentioning

confidence: 99%

“…Advanced state estimation and prediction methods have been developed that use these data ( 1 , 2 ). Some of these methods have already been applied in controllers to optimize traffic conditions proactively ( 3 , 4 ). To benefit fully from these techniques in signalized traffic controllers, it is important to look at the quality of the estimated and predicted input quantities in relation to the performance of the controllers.…”

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confidence: 99%

Sensitivity Analysis to Define Guidelines for Predictive Control Design

Poelman

Hegyi

Verbraeck

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Signalized traffic control is important in traffic management to reduce congestion in urban areas. With recent technological developments, more data have become available to the controllers and advanced state estimation and prediction methods have been developed that use these data. To fully benefit from these techniques in the design of signalized traffic controllers, it is important to look at the quality of the estimated and predicted input quantities in relation to the performance of the controllers. Therefore, in this paper, a general framework for sensitivity analysis is proposed, to analyze the effect of erroneous input quantities on the performance of different types of signalized traffic control. The framework is illustrated for predictive control with different adaptivity levels. Experimental relations between the performance of the control system and the prediction horizon are obtained for perfect and erroneous predictions. The results show that prediction improves the performance of a signalized traffic controller, even in most of the cases with erroneous input data. Moreover, controllers with high adaptivity seem to outperform controllers with low adaptivity, under both perfect and erroneous predictions. The outcome of the sensitivity analysis contributes to understanding the relations between information quality and performance of signalized traffic control. In the design phase of a controller, this insight can be used to make choices on the length of the prediction horizon, the level of adaptivity of the controller, the representativeness of the objective of the control system, and the input quantities that need to be estimated and predicted the most accurately.

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A Test Bed for Multi-Agent Control Systems in Road Traffic Management

Cited by 17 publications

References 13 publications

A review on agent-based technology for traffic and transportation

A review on agent-based technology for traffic and transportation

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Sensitivity Analysis to Define Guidelines for Predictive Control Design

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