Traffic Flow Prediction Based on Long Short Term Memory Network

被引：0

作者：

Li, Yongfu ^{[1
]}

Wu, Xiaolong ^{[1
]}

机构：

[1] Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China

来源：

2018 CHINESE AUTOMATION CONGRESS (CAC) | 2018年

基金：

中国国家自然科学基金;

关键词：

long short term memory; support vector machine; radial basis function; traffic flow predict; NEURAL-NETWORK; MODEL;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study proposes a traffic flow prediction method based on long short term memory (LSTM) network. Firstly, traffic date is preprocessed by time series method. Then a traffic flow prediction algorithm framework based on LSTM arm was proposed to improve the accuracy of traffic forecast and compare algorithm differences between LSTM, support vector machine (SVM) and radial basis function (RBF). In the last part, a reliable experiment was designed. The experimental results verify the superiority performance of LSTM over SVM and RBF in traffic flow prediction.

引用

页码：1157 / 1162

页数：6

共 24 条

[1] A parametric model for the distribution of the angle of arrival and the associated correlation function and power spectrum at the mobile station [J].

Abdi, A ;

Barger, JA ;

Kaveh, M .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2002, 51 (03) :425-434

[2]

Box G. E., 1970, J AM STAT ASSOC, V65, P1509, DOI DOI 10.1080/01621459.1970.10481180

[3] Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg-Marquardt Algorithm [J].

Chan, Kit Yan ;

Dillon, Tharam S. ;

Singh, Jaipal ;

Chang, Elizabeth .

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2012, 13 (02) :644-654

[4] Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method [J].

Cheng, Anyu ;

Jiang, Xiao ;

Li, Yongfu ;

Zhang, Chao ;

Zhu, Hao .

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 466 :422-434

[5] Traffic prediction using multivariate nonparametric regression [J].

Clark, S .

JOURNAL OF TRANSPORTATION ENGINEERING, 2003, 129 (02) :161-168

[6]

CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411

[7] LSTM: A Search Space Odyssey [J].

Greff, Klaus ;

Srivastava, Rupesh K. ;

Koutnik, Jan ;

Steunebrink, Bas R. ;

Schmidhuber, Juergen .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (10) :2222-2232

[8] Adaptive Computation Algorithm for RBF Neural Network [J].

Han, Hong-Gui ;

Qiao, Jun-Fei .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2012, 23 (02) :342-347

[9] Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach [J].

He, Ying ;

Yu, F. Richard ;

Zhao, Nan ;

Leung, Victor C. M. ;

Yin, Hongxi .

IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (12) :31-37

[10] A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation [J].

Huang, GB ;

Saratchandran, P ;

Sundararajan, N .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2005, 16 (01) :57-67

← 1 2 3 →