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

Seungyo Ryu, Dongseung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
EditorsVladimir 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
PublisherIEEE Computer Society
Pages110-114
Number of pages5
ISBN (Electronic)9781728126074
DOIs
Publication statusPublished - 2019 Jul
Event43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
Duration: 2019 Jul 152019 Jul 19

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2
ISSN (Print)0730-3157

Conference

Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
CountryUnited States
CityMilwaukee
Period19/7/1519/7/19

Keywords

  • Deep learning
  • Intelligent transport system
  • Traffic forecasting
  • Transportation network
  • Weather factor

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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  • 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