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On-ramp local control with neural network method

Highway system is a strongly nonlinear system. Owing to the fact that neural network has good nonlinear approximation properties and anti-jamming capability, the neural network and PID control algorithm are introduced to the freeway on-ramp control, by adjusting the on-ramp rate to maintain the desired traffic density on the main highway. The stability of the highway system will be enhanced owing to the fact that RBF algorithm can overcome the disadvantage of conventional BP algorithm and classical ALINEA control strategy, and the anti-perturbation ability will also become stronger. Simulation results have shown that combining the neural network and PID control technology can relieve traffic congestion of the highway mainline.