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Measuring control delay at signalized intersections accurately is very important for designing and operating traffic control systems. At present, we usually maintain control delay through observing on the spot or model calculating. However, the control delays obtained by these methods are not accurately and in real time. In the recent years, the development of vehicle infrastructure integration system...
Traffic system is time-varying and complicated so that traditional signal control methods can't meet the demand of modern traffic management. This paper proposes a novel control model with multi-agent architecture to improve the traffic signal control efficiency and relieve traffic congestion. First, we present the structure of a single agent which consists of sense module, execution module, communication...
In this paper an area signal control model based on temporal planning is presented. Every activity of area signal control is modeled using planning domain definition language, and then the domain model of area signal control is established. Aiming at the representation problem of resource and time in the area signal control model, we extended the basic activity model by adding resource constraint...
This paper applies fuzzy theory and machine learning in the process of intersection signal control. It provides a fuzzy traffic signal control approach based on Particle Swarm Optimization for intersection signal control. Through fuzzy classifying traffic flow in under control intersection and adjacent intersection, this paper puts decision schemes of signal control in different conditions as rule-set...
Multi-agent system (MAS) has been proven to be a useful domain for solving complex coordination and control problems, involving large numbers of autonomous entities interacting in a dynamic environment. Traffic control is one such problem. In this paper, a kind of architecture based on MAS is presented to implement simplified traffic control in a pro-active way by trying to predict and avoid road...
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