A Multi-features Short-term Traffic Flow Prediction Method Based on SDZ-GRU

doi:10.3969/j.issn.1006-2475.2019.10.012

Abstract

Abstract: This paper proposes a multi-features short-term traffic flow prediction method based on SDZ-GRU(SGMTFP) to solve the problems that the current short-term traffic flow prediction methods have large errors and only rely on time-series data. This method adds a series of auxiliary data, such as time information, to the existing time-series data. Also, Surprisal-Driven Zoneout (SDZ) is applied to Gated Recurrent Unit (GRU) to make it a new RNN unit called SDZ-GRU. Through the rolling nested cross-validation experiment, the SGMTFP method proposed in this paper is 7.68% and 14.55% lower than the conventional GRU in terms of root-mean-square error and mean absolute error respectively. In addition, since the SGMTFP method adds auxiliary features, compared with the case without auxiliary features, the root-mean-square error and mean absolute error decrease by 10.9% and 15.1%, respectively. The experimental results show that this method can effectively reduce the error.

Key words: short-term traffic flow prediction, SDZ, GRU, auxiliary features

CLC Number:

TP302

LYU Tian. A Multi-features Short-term Traffic Flow Prediction Method Based on SDZ-GRU[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.10.012.

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