Improved Social Emotion Optimization Algorithm for Short-Term Traffic Flow Forecasting Based on Back-Propagation Neural Network

Abstract

The back-propagation neural network (BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm (SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation (PSOBP) and simulated annealing particle swarm optimization back-propagation (SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation (SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation (MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.