

Variable Speed Control Method of Freeway Mainline based on IGA-DRFNN
Abstract
Speed control of freeway mainline is very necessary to guarantee stable traffic flow and reduce traffic accident. In this paper, a variable speed control method of freeway mainline based on dynamic recurrent fuzzy neural network (DRFNN) with immune genetic algorithm (IGA) optimization is proposed. This method fully considers the traffic flow condition, vehicle type composition, road condition as well as weather condition. The IGA optimization make the DRFNN has a certain learning and self-adaptation capability, which improves the anti-interference and robustness and leads to quick convergence speed and low training error. Analysis and numerical results from application to realistic scenario demonstrate that this method has higher sensitivity under complex external influence conditions compared with relative methods and is more suitable for conducting efficient real-time speed control of freeway mainline.
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