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http://dx.doi.org/10.12815/kits.2019.18.4.44

Prediction of Traffic Congestion in Seoul by Deep Neural Network  

Kim, Dong Hyun (Department of Computer Engineering, University of Hongik)
Hwang, Kee Yeon (Department of Urban Engineering., University of Hongik)
Yoon, Young (Department of Computer Engineering, University of Hongik)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.4, 2019 , pp. 44-57 More about this Journal
Abstract
Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.
Keywords
Deep Neural Networks; Machine Learning; Prediction of Traffic Congestion; Big Data Analysis; Multi-lateral Context Awareness;
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Times Cited By KSCI : 2  (Citation Analysis)
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