The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide] efficient traffic regulation. Therefore, dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This study devised a multi-agent architecture, the Adaptive and Cooperative Traffic light Agent Model (ACTAM), for a decentralized traffic signal control system. The proposed architecture comprises a data storage and communication layer, a traffic regulation factor processing layer, and a decision-making layer. This study focused on utilizing the cooperation of multi-agents and the prediction mechanism of our architecture, the Forecast Module, to forecast future traffic volume in each individual intersection. The Forecast Module is designed to forecast traffic volume in an intersection via multi-agent cooperation by exchanging traffic volume information for adjacent intersections, since vehicles passing through nearby intersections were believed to significantly influence the traffic volume of specific intersections. The proposed architecture can achieve dynamic traffic signal control. Thus, total delay time of the traffic network under ACTAM can be reduced by 37% compared to the conventional fixed sequence traffic signal control strategy. Consequently, traffic congestion in urban areas can be alleviated by adopting ACTAM.

We consider a dynamically reconfigureable network where dynamically changing traffic is offered. Rearrangement and adjustment of network capacity can be performed to maintain Quality of Service (QoS) requirements for different traffic classes in the dynamic traffic environment. In this work, we consider the case of a single, dynamic traffic class scenario in a loss mode environment. We have developed a numerical, analytical tool which models the dynamically changing network traffic environment using a time-varying, fluid-flow, differential equation; of which we can use to study the impact of adaptive capacity adjustment control schemes. We present several capacity adjustment control schemes including schemes which use blocking and system utilization as means to calculate when and how much adjustment should be made. Through numerical studies, we show that a purely blocking-based capacity adjustment control scheme with a preset adjustment value can be very sensitive to capacity changes and can lead to network instability. We also show that schemes, that uses system utilization as a means to calculate the amount of capacity adjustment needed, is consistently stable for the load scenarios considered. Finally, we introduce a minimum time interval threshold between adjustments, which can avoid network instability, in the cases where the results showed that capacity adjustment had been performed too often.