Next Article in Journal
SDN-Based Intrusion Detection System for Early Detection and Mitigation of DDoS Attacks
Previous Article in Journal
Discrete Wavelet Packet Transform-Based Industrial Digital Wireless Communication Systems
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(3), 105; https://doi.org/10.3390/info10030105

Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction

1,2,*
and
1
1
Institute of IOT and IT-based Industrialization, Xi’an University of Post and Telecommunications, Xi’an 710061, China
2
Shaanxi Provincial Information Engineering Research Institute, Xi’an 710075, China
*
Author to whom correspondence should be addressed.
Received: 2 February 2019 / Revised: 19 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
(This article belongs to the Section Artificial Intelligence)
Full-Text   |   PDF [4073 KB, uploaded 8 March 2019]   |  

Abstract

The existing short-term traffic flow prediction models fail to provide precise prediction results and consider the impact of different traffic conditions on the prediction results in an actual traffic network. To solve these problems, a hybrid Long Short–Term Memory (LSTM) neural network is proposed, based on the LSTM model. Then, the structure and parameters of the hybrid LSTM neural network are optimized experimentally for different traffic conditions, and the final model is compared with the other typical models. It is found that the prediction error of the hybrid LSTM model is obviously less than those of the other models, but the running time of the hybrid LSTM model is only slightly longer than that of the LSTM model. Based on the hybrid LSTM model, the vehicle flows of each road section and intersection in the actual traffic network are further predicted. The results show that the maximum relative error between the actual and predictive vehicle flows of each road section is 1.03%, and the maximum relative error between the actual and predictive vehicle flows of each road intersection is 1.18%. Hence, the hybrid LSTM model is closer to the accuracy and real-time requirements of short-term traffic flow prediction, and suitable for different traffic conditions in the actual traffic network. View Full-Text
Keywords: short-term traffic flow prediction; Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM); hybrid LSTM short-term traffic flow prediction; Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM); hybrid LSTM
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Xiao, Y.; Yin, Y. Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction. Information 2019, 10, 105.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top