Short-term Traffic Flow Prediction Based on Deep Learning

被引：0
|
作者：
Wang X.-X. [1 ]
Xu L.-H. [1 ]
机构：
[1] School of Civil Engineering and Transportation, South China University of Technology, Guangzhou
来源：
Xu, Lun-Hui (lhx_scut@163.com) | 2018年 / Science Press卷 / 18期
基金：
中国国家自然科学基金;
关键词：
Deep learning; LSTM-RNN; Time series; Traffic engineering; Traffic flow prediction;
D O I：
10.16097/j.cnki.1009-6744.2018.01.012
中图分类号：
学科分类号：
摘要：
This paper proposes a traffic flow time series prediction model for urban expressway based on LSTMRNN under deep learning framework. First, we refactor the traffic time series with integrated spatial and temporal correlation of traffic flow, making LSTM-RNN obtain and strengthen the ability of data mining. Next, network depth is determined by both precision and timeliness during model designing. And then, we take use of Keras based on TensorFlow to implement LSTM- RNN with building model layer by layer and regulating all the parameters subtly. We validate the model utilizing the measured data from real express way, and implement local model saving and updating regularly according to the prediction accuracy. It is proved that the proposed model performs an accurate prediction for short-term traffic flow which is not restricted by the training sample size to a large extent. Meanwhile, the extensibility and practicability of the model is improved significantly. Copyright © 2018 by Science Press.
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页码：81 / 88
页数：7
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