Short-term inbound passenger flow prediction for urban rail transit by integrating spatiotemporal features

Ming He; Jing Zuo

doi:10.1117/12.3054620

20 December 2024 Short-term inbound passenger flow prediction for urban rail transit by integrating spatiotemporal features

Ming He, Jing Zuo

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134213T (2024) https://doi.org/10.1117/12.3054620
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

Accurate subway passenger flow prediction is crucial for intelligent transportation systems to manage traffic, optimize operations, and plan infrastructure. Since passenger flow is affected by factors like station location and date type, further research on short-term high-frequency prediction is needed. This paper proposes a method combining Graph Convolutional Networks (GCN) for spatial dependencies and Informer models for long-term temporal dependencies to capture spatiotemporal relationships. By fusing these features in a fully connected layer, the approach achieves precise passenger flow forecasts. Experiments on Hangzhou Metro Line 1 show that this method is robust and accurate.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming He and Jing Zuo "Short-term inbound passenger flow prediction for urban rail transit by integrating spatiotemporal features", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134213T (20 December 2024); https://doi.org/10.1117/12.3054620

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