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.
Long Short-Term Memory Recurrent Neural Network for Urban Traffic Prediction: A Case Study of Seoul
01.11.2018
2263820 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DOAJ | 2024
|Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network
Transportation Research Record | 2019
|