Trafficformer: A Transformer-based Traffic Predictor

Junseo Ko; Jeewoo Yoon; Daejin Choi; Eunil Park; Sangheon Pack; Jinyoung Han

doi:10.1109/ICCE53296.2022.9730205

Trafficformer: A Transformer-based Traffic Predictor

Junseo Ko
, Jeewoo Yoon
, Daejin Choi
, Eunil Park
, Sangheon Pack
, Jinyoung Han

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

We propose a deep learning model that can pre-dict future traffic. The proposed Transformer-based model is evaluated using the visit data of popular Wikipedia pages for more than 2 years through multiple accessing devices such as mobile and desktop. The experiment results demonstrate that the proposed model can predict the future traffic with high accuracy.

Original language	English
Title of host publication	2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665441544
DOIs	https://doi.org/10.1109/ICCE53296.2022.9730205
State	Published - 2022
Event	2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States Duration: 7 Jan 2022 → 9 Jan 2022

Publication series

Name	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume	2022-January
ISSN (Print)	0747-668X

Conference

Conference	2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Country/Territory	United States
City	Virtual, Online
Period	7/01/22 → 9/01/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Access to Document

10.1109/ICCE53296.2022.9730205

Cite this

Ko, J., Yoon, J., Choi, D., Park, E., Pack, S., & Han, J. (2022). Trafficformer: A Transformer-based Traffic Predictor. In 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE53296.2022.9730205

@inproceedings{df6410ebd6aa485a89614956bacfa8f8,

title = "Trafficformer: A Transformer-based Traffic Predictor",

abstract = "We propose a deep learning model that can pre-dict future traffic. The proposed Transformer-based model is evaluated using the visit data of popular Wikipedia pages for more than 2 years through multiple accessing devices such as mobile and desktop. The experiment results demonstrate that the proposed model can predict the future traffic with high accuracy.",

author = "Junseo Ko and Jeewoo Yoon and Daejin Choi and Eunil Park and Sangheon Pack and Jinyoung Han",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 ; Conference date: 07-01-2022 Through 09-01-2022",

year = "2022",

doi = "10.1109/ICCE53296.2022.9730205",

language = "English",

series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE International Conference on Consumer Electronics, ICCE 2022",

}

Ko, J, Yoon, J, Choi, D, Park, E, Pack, S & Han, J 2022, Trafficformer: A Transformer-based Traffic Predictor. in 2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Consumer Electronics, ICCE 2022, Virtual, Online, United States, 7/01/22. https://doi.org/10.1109/ICCE53296.2022.9730205

Trafficformer: A Transformer-based Traffic Predictor. / Ko, Junseo; Yoon, Jeewoo; Choi, Daejin et al.
2022 IEEE International Conference on Consumer Electronics, ICCE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Ko, Junseo

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AU - Choi, Daejin

AU - Park, Eunil

AU - Pack, Sangheon

AU - Han, Jinyoung

PY - 2022

Y1 - 2022

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AB - We propose a deep learning model that can pre-dict future traffic. The proposed Transformer-based model is evaluated using the visit data of popular Wikipedia pages for more than 2 years through multiple accessing devices such as mobile and desktop. The experiment results demonstrate that the proposed model can predict the future traffic with high accuracy.

UR - https://www.scopus.com/pages/publications/85127048038

U2 - 10.1109/ICCE53296.2022.9730205

DO - 10.1109/ICCE53296.2022.9730205

M3 - Conference contribution

AN - SCOPUS:85127048038

T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics

BT - 2022 IEEE International Conference on Consumer Electronics, ICCE 2022

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 7 January 2022 through 9 January 2022

ER -

Trafficformer: A Transformer-based Traffic Predictor

Abstract

Publication series

Conference

Bibliographical note

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