Title: Short-term traffic flow prediction model based on deep learning regression algorithm

Authors: Yang Zhang; Dong-rong Xin

Addresses: School of Transportation, Fujian University of Technology, Fuzhou, 350118, China ' School of Transportation, Fujian University of Technology, Fuzhou, 350118, China

Abstract: In view of the problem that the short-term traffic flow prediction under the condition of unsteady traffic flow, such as low precision and over-reliance on large sample historical data, proposing a novel short-term traffic-flow prediction method based on deep learning support vector regression (DL-SVR). A framework of the DL-SVR is built with a restricted Boltzmann machine (RBM) visible inputting layer, which is connected with several intermediate operating networks, and a radial SVR output layer. In addition, a T mutation particle swarm optimisation algorithm is proposed to select the important parameter in DL-SVR. Experimental results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the proposed short-term traffic-flow prediction method are better than other classic algorithms, and the real time also can meet the needs of practical use.

Keywords: deep learning; SVR; support vector regression; short-term traffic flow; ANN; artificial neural network.

DOI: 10.1504/IJCSM.2021.118796

International Journal of Computing Science and Mathematics, 2021 Vol.14 No.2, pp.155 - 166

Received: 29 Jul 2019
Accepted: 09 Mar 2020
Published online: 08 Nov 2021 *

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