상세정보
학술대회
Long Short-Term Memory Recurrent Neural Network for Urban Traffic Prediction: A Case Study of Seoul
- 발행일
- 201811
- 출처
- International Conference on Intelligent Transportation Systems (ITSC) 2018, pp.1279-1284
- 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.
- KSP 제안 키워드
- Case studies, Comparative analysis, GPU computing, Improved Accuracy, Intelligent Transport Systems(ITS), Learning approach, Long short-term memory recurrent neural network, Long-short term memory(LSTM), Recurrent Neural Network(RNN), Time series data, Traffic congestion