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학술대회 Long Short-Term Memory Recurrent Neural Network for Urban Traffic Prediction: A Case Study of Seoul
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저자
이용주, 민옥기
발행일
201811
출처
International Conference on Intelligent Transportation Systems (ITSC) 2018, pp.1279-1284
DOI
https://dx.doi.org/10.1109/ITSC.2018.8569620
협약과제
18HS4100, 도시 교통 문제 개선을 위한 클라우드 기반 트래픽 예측 시뮬레이션 SW 기술 개발, 민옥기
초록
Traffic prediction is an important research issue for solving the traffic congestion problems in an Intelligent Transportation System (ITS). In urban areas, traffic congestion has increasingly become a difficult problem. In recent years, abundant traffic data and powerful GPU computing have led to improved accuracy in traffic data analysis via deep learning approaches. In this paper, we propose a long short-term memory recurrent neural network for urban traffic prediction in a case study of Seoul, Korea. The proposed method combines various kinds of time-series data into a model and we conduct comparative analysis using synthetic and real datasets. Our model confirms the proposed method can achieve better accuracy.
키워드
Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Traffic Prediction
KSP 제안 키워드
Case studies, Comparative analysis, GPU computing, Improved Accuracy, Intelligent Transport Systems(ITS), Learning approach, Long short-term memory recurrent neural network, Recurrent Neural Network(RNN), Time series data, Traffic congestion, Traffic data analysis