Journal
2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 2
Volume -, Issue -, Pages 110-114Publisher
IEEE COMPUTER SOC
DOI: 10.1109/COMPSAC.2019.10192
Keywords
traffic forecasting; intelligent transport system; deep learning; transportation network; weather factor
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2018R1D1A1B07046195]
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We propose a highway traffic forecasting system that informs the traffic condition of highways from a few in 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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