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http://dx.doi.org/10.12815/kits.2020.19.1.1

Development of Traffic Speed Prediction Model Reflecting Spatio-temporal Impact based on Deep Neural Network  

Kim, Youngchan (Dept. of Transportation Eng, Univ. of Seoul)
Kim, Junwon (Dept. of Transportation Eng, Univ. of Seoul)
Han, Yohee (Dept. of Transportation Eng, Univ. of Seoul)
Kim, Jongjun (Dept. of Transportation Eng, Univ. of Seoul)
Hwang, Jewoong (Dept. of Transportation Eng, Univ. of Seoul)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.1, 2020 , pp. 1-16 More about this Journal
Abstract
With the advent of the fourth industrial revolution era, there has been a growing interest in deep learning using big data, and studies using deep learning have been actively conducted in various fields. In the transportation sector, there are many advantages to using deep learning in research as much as using deep traffic big data. In this study, a short -term travel speed prediction model using LSTM, a deep learning technique, was constructed to predict the travel speed. The LSTM model suitable for time series prediction was selected considering that the travel speed data, which is used for prediction, is time series data. In order to predict the travel speed more precisely, we constructed a model that reflects both temporal and spatial effects. The model is a short-term prediction model that predicts after one hour. For the analysis data, the 5minute travel speed collected from the Seoul Transportation Information Center was used, and the analysis section was selected as a part of Gangnam where traffic was congested.
Keywords
Deep learning; LSTM; Travel speed prediction; Big data; The fourth industrial revolution;
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