A metro traffic flow forecasting system coupling singular spectrum analysis, deep bidirectional LSTM networks, and ensemble strategy

Rui Yang; Hui Liu; Yanfei Li

doi:10.1117/12.2673719

22 May 2023 A metro traffic flow forecasting system coupling singular spectrum analysis, deep bidirectional LSTM networks, and ensemble strategy

Rui Yang, Hui Liu, Yanfei Li

Author Affiliations +

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400I (2023) https://doi.org/10.1117/12.2673719
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

With the deepening and acceleration of urbanization and motorization in China, the problem of urban traffic networks represented by traffic congestion has become increasingly prominent. As an important part of the urban rail transit system, the metro plays a significant role in alleviating the pressure on road traffic. Accurate metro traffic flow forecasting can provide important guidance for the optimization of urban transportation network layout. In this paper, a novel traffic system coupling singular spectrum analysis, deep bidirectional LSTM networks, and ensemble strategy is established for metro traffic flow forecasting. Real metro traffic flow data from three different areas of Shanghai are used to verify the validity of the model. The experimental results show that the proposed coupling system can accurately predict the changes of metro traffic flow.

Citation Download Citation

Rui Yang, Hui Liu, and Yanfei Li "A metro traffic flow forecasting system coupling singular spectrum analysis, deep bidirectional LSTM networks, and ensemble strategy", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400I (22 May 2023); https://doi.org/10.1117/12.2673719

ACCESS THE FULL ARTICLE

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