@inproceedings{078dd08bb9dd465599d2c823d8e35615,
title = "Intelligent highway traffic forecast based on deep learning and restructured road models",
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",
year = "2019",
month = jul,
doi = "10.1109/COMPSAC.2019.10192",
language = "English",
series = "Proceedings - International Computer Software and Applications Conference",
publisher = "IEEE Computer Society",
pages = "110--114",
editor = "Vladimir Getov and Jean-Luc Gaudiot and Nariyoshi Yamai and Stelvio Cimato and Morris Chang and Yuuichi Teranishi and Ji-Jiang Yang and Leong, {Hong Va} and Hossian Shahriar and Michiharu Takemoto and Dave Towey and Hiroki Takakura and Atilla Elci and Susumu Takeuchi and Satish Puri",
booktitle = "Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019",
}