Study On Freeway Ramp Control Based On BP Neural Networks

Nowadays recurrent and nonrecurrent congestion on freeways is increasing, which causes delays, environmental pollution and traffic unsafety. Ramp metering is one of the most efficient and wide-used means to alleviate this phenomenon. The efficient and robust control of the on-ramp is a hot research topic with great practical value. Due to the traits of nonlinear, capacity of study, self adapting and anti-interference, neural network is suitable for the control of ramp metering.Firstly, the background of ramp control and neural networks was introduced. Back-propagation (BP) neural network has many merits, such as simple structure, easy to calculate and multi-input/multi output, which makes it to be greatly applied in control. Therefore, in this thesis the algorithm of BP was introduced and the design of BP control was discussed in detail, including the design of input/output (I/O) layer, the processing of the data of I/O, the selection of the number of hide layer and learning rate etc.Secondly, two neural networks controllers for the ramp control are developed, which are the direct controller and the PI controller. The controller is considered as part of the control system to overcome the difficult of sample collecting. The neural networks controller is trained by adjusted BP algorithm. Preceding traffic conditions is put into the direct networks controller, whose outputs are ramp metering rate. The difference between the output and the reference is put into the PI neural networks controller. The control effect is simulated with random test data and compared with the ALINEA, demand-capacity control and without control. It shows that the neural networks control has robustness, and the mainline traffic density is more stable.Finally, the neural networks controller is improved when the queue of vehicles on the ramp is considered. Upon which two control techniques are brought out to reduce the queue length. The control effect is simulated and compared with ALINEA control and without control. It's shown that the methods developed are helpful to reduce the queue of vehicles and the interference with surface street traffic.In a word, BP neural networks controllers in the freeway ramp control can avoid freeway jam greatly, make mainline traffic condition stable and diminish the queue of vehicles on the ramp.