Abstract
Signalized intersection delay is an important evaluation index of the signalized intersection capacity and service level. This paper presents a signalized intersection delay model for signalized intersection. The model uses the methods which combine the RBF neural network with stochastic service system, and also uses MATLAB for computing simulation. It discusses the characteristics of the operation in the arrival and leaving process. The simulation results show that the RBF network can identify nonlinear complex systems and is more suitable for the short-term traffic forecasts. The theory of stochastic service system is practicable in vehicle delay and operating characteristic. It can provide a theoretical basis for optimizing the signal and is better to adapt the continual changes of traffic conditions than traditional methods.
Published Version
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