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Conference Paper Long Short-Term Memory Recurrent Neural Network for Urban Traffic Prediction: A Case Study of Seoul
Cited 20 time in scopus Share share facebook twitter linkedin kakaostory
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
DOI
https://dx.doi.org/10.1109/ITSC.2018.8569620
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)