CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13163 > Article
Paper
5 June 2024 Short-term traffic flow prediction based on combination of SSA and LSTM
Guangxun E., Rui Song, Yutao Chang, Qun Liu, Kun Zhou
Author Affiliations +
Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131635J (2024) https://doi.org/10.1117/12.3030183
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China
Abstract
Short-term traffic flow prediction stands as a critical and highly topical research subject within the intelligent transportation system (ITS) domain. Owing to its inherent uncertainty and complexity, the task of predicting short-term traffic flow consistently presents a formidable challenge. Data driven methods have been achieved a success in the traffic prediction filed. In this paper, to enhance the precision of short-term traffic flow forecasting, we propose a hybrid model that integrates Singular Spectrum Analysis (SSA) with Long Short-Term Memory (LSTM) networks, denoted as SSA-LSTM. Initially, the traffic flow time series data is decomposed by SSA. Subsequently, the reconstructed traffic flow data is used to train the LSTM model. The model's efficacy is then validated through case analysis using real data collected from an expressway station in Jinan, Shandong, China. Finally, the SSA-LSTM model is compared with several models commonly used in short-term traffic flow prediction, including SSA and K-nearest neighbor (SSA-KNN), SSA and support vector regression (SSA-SVR), as well as single LSTM, KNN, and SVR models. The results of error analysis show that the proposed model (SSA-LSTM) has the best performance.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Guangxun E., Rui Song, Yutao Chang, Qun Liu, and Kun Zhou "Short-term traffic flow prediction based on combination of SSA and LSTM", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131635J (5 June 2024); https://doi.org/10.1117/12.3030183
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 10 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Performance modeling
Matrices
Spectrum analysis
Neural networks
Signal filtering
Singular value decomposition
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top