Intelligent highway traffic forecast based on deep learning and restructured road models

Seungyo Ryu; Dongseung Kim

doi:10.1109/COMPSAC.2019.10192

Intelligent highway traffic forecast based on deep learning and restructured road models

Seungyo Ryu, Dongseung Kim

GREEN SCHOOL (Graduate School of Energy and Environment)

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

3 Citations (Scopus)

Abstract

We propose a highway traffic forecasting system that informs the traffic condition of highways from a few minutes to several months ahead. It can reflect the weather information of the regions of roads in the traffic data computation. We develop various road models to represent separate points of the highways based on traffic characteristics such as interchange, exit, endpoint, etc. Experimental results show our system outperforms a generic convolutional network model with 97.6% accuracy of travel-time prediction and the reduction by 30% of computing time for a moderate sized highway network.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
Editors	Vladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
Publisher	IEEE Computer Society
Pages	110-114
Number of pages	5
ISBN (Electronic)	9781728126074
DOIs	https://doi.org/10.1109/COMPSAC.2019.10192
Publication status	Published - 2019 Jul
Event	43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States Duration: 2019 Jul 15 → 2019 Jul 19

Publication series

Name	Proceedings - International Computer Software and Applications Conference
Volume	2
ISSN (Print)	0730-3157

Conference

Conference	43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
Country/Territory	United States
City	Milwaukee
Period	19/7/15 → 19/7/19

Keywords

Deep learning
Intelligent transport system
Traffic forecasting
Transportation network
Weather factor

ASJC Scopus subject areas

Software
Computer Science Applications

Access to Document

10.1109/COMPSAC.2019.10192

Cite this

Ryu, S., & Kim, D. (2019). Intelligent highway traffic forecast based on deep learning and restructured road models. In V. Getov, J-L. Gaudiot, N. Yamai, S. Cimato, M. Chang, Y. Teranishi, J-J. Yang, H. V. Leong, H. Shahriar, M. Takemoto, D. Towey, H. Takakura, A. Elci, S. Takeuchi, & S. Puri (Eds.), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 (pp. 110-114). [8754393] (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2019.10192

Intelligent highway traffic forecast based on deep learning and restructured road models. / Ryu, Seungyo; Kim, Dongseung.

Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. ed. / Vladimir Getov; Jean-Luc Gaudiot; Nariyoshi Yamai; Stelvio Cimato; Morris Chang; Yuuichi Teranishi; Ji-Jiang Yang; Hong Va Leong; Hossian Shahriar; Michiharu Takemoto; Dave Towey; Hiroki Takakura; Atilla Elci; Susumu Takeuchi; Satish Puri. IEEE Computer Society, 2019. p. 110-114 8754393 (Proceedings - International Computer Software and Applications Conference; Vol. 2).

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

Ryu, S & Kim, D 2019, Intelligent highway traffic forecast based on deep learning and restructured road models. in V Getov, J-L Gaudiot, N Yamai, S Cimato, M Chang, Y Teranishi, J-J Yang, HV Leong, H Shahriar, M Takemoto, D Towey, H Takakura, A Elci, S Takeuchi & S Puri (eds), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019., 8754393, Proceedings - International Computer Software and Applications Conference, vol. 2, IEEE Computer Society, pp. 110-114, 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019, Milwaukee, United States, 19/7/15. https://doi.org/10.1109/COMPSAC.2019.10192

Ryu S, Kim D. Intelligent highway traffic forecast based on deep learning and restructured road models. In Getov V, Gaudiot J-L, Yamai N, Cimato S, Chang M, Teranishi Y, Yang J-J, Leong HV, Shahriar H, Takemoto M, Towey D, Takakura H, Elci A, Takeuchi S, Puri S, editors, Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. IEEE Computer Society. 2019. p. 110-114. 8754393. (Proceedings - International Computer Software and Applications Conference). https://doi.org/10.1109/COMPSAC.2019.10192

Ryu, Seungyo ; Kim, Dongseung. / Intelligent highway traffic forecast based on deep learning and restructured road models. Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. editor / Vladimir Getov ; Jean-Luc Gaudiot ; Nariyoshi Yamai ; Stelvio Cimato ; Morris Chang ; Yuuichi Teranishi ; Ji-Jiang Yang ; Hong Va Leong ; Hossian Shahriar ; Michiharu Takemoto ; Dave Towey ; Hiroki Takakura ; Atilla Elci ; Susumu Takeuchi ; Satish Puri. IEEE Computer Society, 2019. pp. 110-114 (Proceedings - International Computer Software and Applications Conference).

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abstract = "We propose a highway traffic forecasting system that informs the traffic condition of highways from a few minutes to several months ahead. It can reflect the weather information of the regions of roads in the traffic data computation. We develop various road models to represent separate points of the highways based on traffic characteristics such as interchange, exit, endpoint, etc. Experimental results show our system outperforms a generic convolutional network model with 97.6% accuracy of travel-time prediction and the reduction by 30% of computing time for a moderate sized highway network.",

keywords = "Deep learning, Intelligent transport system, Traffic forecasting, Transportation network, Weather factor",

author = "Seungyo Ryu and Dongseung Kim",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: NRF-2018R1D1A1B07046195). Publisher Copyright: {\textcopyright} 2019 IEEE; 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 ; Conference date: 15-07-2019 Through 19-07-2019",

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AB - We propose a highway traffic forecasting system that informs the traffic condition of highways from a few minutes to several months ahead. It can reflect the weather information of the regions of roads in the traffic data computation. We develop various road models to represent separate points of the highways based on traffic characteristics such as interchange, exit, endpoint, etc. Experimental results show our system outperforms a generic convolutional network model with 97.6% accuracy of travel-time prediction and the reduction by 30% of computing time for a moderate sized highway network.

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Intelligent highway traffic forecast based on deep learning and restructured road models

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Keywords

ASJC Scopus subject areas

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