Hongsuk Yi  Khac-Hoai Nam Bui  Choong-Nyoung Seon

10.5626/JOK.2020.47.3.292

Deep Learning Long Short Term Memory traffic flow prediction fine dust prediction

Accurate and timely forecasting is an essential step for the successful deployment of smart cities. With the rapid growth of traffic data collected daily, recent studies have focused on deep learning based on long-term short term memory (LSTM) for short-term traffic prediction, especially in urban areas. However, the short-term (five minutes) LSTM model is limited in the real-time nonlinear traffic flow prediction. Moreover, the fine dust prediction based on traffic data is also an emerging issue in this research area. Thus, this paper designs the multiple traffic data-based multi-input/output LSTM framework for supporting medium and long-term prediction. Additionally, a convolutional LSTM (ConvLSTM) model is developed for predicting fine dust flow based on traffic data. Regarding the experiment, we analyzed data from the Vehicle Detection System (VDS) located on major roads in Daejeon City for the evaluation. The experiment indicates promising results for the proposed approach.