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

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, April 14-19

ISBN

9798350344868

Identifier

10.1109/ICASSP48485.2024.10446095

Publisher

IEEE

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICASSP48485.2024.10446095

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