期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：189
期号：6
页码：062033
DOI：10.1088/1755-1315/189/6/062033
语种：English
出版社：IOP Publishing
摘要：To improve the short term traffic prediction precision, this paper proposes an improved Elman neural network (ELMNN) model for the prediction work. The model input includes two parts: the measured flow data and the theoretical flow data obtained by traffic occupancy and speed. Furthermore, in order to capture the inner regularity of the time series data, the theoretical flow data and the measured flow data are both reconstructed using a phase space reconstruction method. Finally, the reconstructed data are put into the improved ELMNN model, which is developed by employing the mind evolution algorithm (MEA). Compared with the original models, the results of the case study show that the proposed model can obviously improve the prediction accuracy.
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