Modeling route representation with mixed-scale hierarchical transformer
Publication Type
Conference Proceeding Article
Publication Date
4-2024
Abstract
Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and interroute features. Experiments on real data confirm RouteMT’s superior performance and versatility.
Keywords
Spatial-temporal data modeling, road sensor network, self-attention model
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Areas of Excellence
Digital transformation
Publication
2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Seoul, April 14-19: Proceedings
First Page
5295
Last Page
5299
ISBN
9798350344868
Identifier
10.1109/ICASSP48485.2024.10446095
Publisher
IEEE
City or Country
Pistacataway
Citation
ZHANG, Hanyuan; CHEN, Yuqi; ZHANG, Xinyu; JIANG, Qize; LI, Liang; ZHENG, Baihua; and SUN, Weiwei.
Modeling route representation with mixed-scale hierarchical transformer. (2024). 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Seoul, April 14-19: Proceedings. 5295-5299.
Available at: https://ink.library.smu.edu.sg/sis_research/9168
Additional URL
https://doi.org/10.1109/ICASSP48485.2024.10446095
