Book Chapter or Conference Paper Title
Using deep learning to predict short term traffic flow: a systematic literature review
Department of Computer Science
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International Conference on Intelligent Transport Systems
29-30 November 2017
Abstract / Description
This paper systematically reviews Deep Learning-based methods for traffic flow prediction. We extracted 26 articles using a concrete methodology and reviewed them from two perspectives: first, the deep learning architecture used; and second, the datasets and data dimensions incorporated. Recent big data explosion caused by sensors, IoV, IoT and GPS technology needs traffic analytics using deep architectures. This survey reveals that the LSTM (Long Short-Term Memory) Neural Networks are the most commonly used architecture for short term traffic flow prediction due to their inherent ability to handle sequential data. Among the datasets, PeMS is the most commonly used for traffic flow prediction task. Today, Intelligent Transport Systems (ITS) are not limited to temporal data; spatial dimension is also incorporated along with weather data, and traffic sentiments from twitter, Facebook and Instagram to get better results. In the authors’ knowledge, this is the first deep learning review in ITS domain.
Ali, U., & Mahmood, T. (2017, November). Using deep learning to predict short term traffic flow: A systematic literature review. In First International Conference on Intelligent Transport Systems (pp. 90-101). Springer, Cham.
Ali, U., & Mahmood, T. (2017). Using deep learning to predict short term traffic flow: a systematic literature review., 90-101. https://doi.org/10.1007/978-3-319-93710-6_11