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Conference Paper
Long Short-Term Memory Recurrent Neural Network for Urban Traffic Prediction: A Case Study of Seoul
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- Authors
- Yong-Ju Lee, OkGee Min
- Issue Date
- 2018-11
- Citation
- International Conference on Intelligent Transportation Systems (ITSC) 2018, pp.1279-1284
- Language
- English
- Type
- Conference Paper
- Abstract
- 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 Keywords
- Case studies, Comparative analysis, GPU computing, Improved Accuracy, Intelligent transportation systems, Learning approach, Long short-term memory recurrent neural network, Time series data, Traffic congestion, Traffic data analysis, deep learning(DL)