Traffic Flow Prediction Based on Deep Learning

Authors

Mingyu Liu, 1. Department of Civil Engineering, Tsinghua University, Beijing 100084, China; ;
Jianping Wu, 1. Department of Civil Engineering, Tsinghua University, Beijing 100084, China; ;
Yubo Wang, 2. Chu Kochen Honors College, Hangzhou 310000, China; ;
He Lei, 3. Beijing Traffic Management Bureau, Beijing 100037, China;

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.

Recommended Citation

Liu, Mingyu; Wu, Jianping; Wang, Yubo; and Lei, He (2019) "Traffic Flow Prediction Based on Deep Learning," Journal of System Simulation: Vol. 30: Iss. 11, Article 7.
DOI: 10.16182/j.issn1004731x.joss.201811007
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/7

First Page

4100

Revised Date

2018-06-29

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811007

Last Page

4106

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201811007