TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective

Xin Jiang; Ke Li; Kai Liu

doi:10.1117/12.3015662

20 February 2024 TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective

Xin Jiang, Ke Li, Kai Liu

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130640X (2024) https://doi.org/10.1117/12.3015662
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

Long-term traffic flow prediction plays a crucial role in intelligent transportation systems. However, the temporal nonstationary nature of data from traffic detector nodes and the interdependence among nodes pose significant challenges in this prediction task. Therefore, we propose a traffic prediction transformer framework, called TPformer, to learn the spatiotemporal correlations of traffic features among traffic detector nodes. The success of applying the transformer to traffic flow prediction relies on the following key factors: 1) The length of both the input sequence and the predicted sequence is equal to the number of detector nodes, facilitating the construction of a node-node attention mechanism. 2) A node triplet encoding is introduced to allocate additional local contextual structural features to each node. 3) The topological structural features of the transportation network are incorporated into the self-attention mechanism, allowing the model to focus more on neighboring nodes when encoding the node traffic feature. Experimental results on two real-world datasets from the Performance Measurement System (PeMS) demonstrate that the proposed model outperforms state-of-the-art baselines in both short-term and long-term prediction tasks.

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

Citation Download Citation

Xin Jiang, Ke Li, and Kai Liu "TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130640X (20 February 2024); https://doi.org/10.1117/12.3015662

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