Traffic Flow Prediction Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.201811007

Abstract

Abstract: Traffic flow prediction is an important component of urban intelligent transportation system. With the development of machine learning and artificial intelligence, deep learning has been applied in traffic engineering area. Gated recurrent unit (GRU) neural network is selected to predict urban traffic flow. Cross-validation method is used to explore the optimal number of gated recurrent units. The GRU model is compared with other three predictors such as support vector regression and evaluated in different performance measurements. The results show that GRU model has better performance in traffic flow prediction than the other three models.

Key words: traffic engineering, GRU, traffic flow prediction, deep learning

CLC Number:

TP391.9

Liu Mingyu, Wu Jianping, Wang Yubo, He Lei. Traffic Flow Prediction Based on Deep Learning[J]. Journal of System Simulation, 2018, 30(11): 4100-4106.

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