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.
ZHANG Jun* (张军), ZHAO Shenwei (赵申卫), WANG Yuanqiang (王远强), ZHU Xinshan (朱新山)
. Improved Social Emotion Optimization Algorithm for Short-Term Traffic Flow Forecasting Based on Back-Propagation Neural Network[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(2)
: 209
-219
.
DOI: 10.1007/s12204-019-2055-9
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