Short-term Traffic Forecasting Based on Grey Neural Network with Particle Swarm Optimization

An accurate and stable short-term traffic forecasting model is very important for intelligent transportation systems (ITS). The forecasting results can be used to relieve traffic congestion and improve the mobility of transportation. This paper proposes a new hybrid model of grey system theory and neural networks with particle swarm optimization, namely, GNN-PSO. The proposed hybrid model can exploit sufficiently the characteristics of grey system model requiring less data, the non-linear map of neural networks and the quick-speed convergence of PSO, and has simpler structure. The GNNPSO model is applied to predict the average speed of vehicle on Barbosa road in Macao. The experiment results show that the proposed model has better performance than grey forecasting model GM(1,1), back-propagation neural network model BPNN, and the combined model of them, i.e., grey neural network model (GNN), on short-term traffic forecasting.

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