Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting.
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Cites background from "Deep Learning: A Generic Approach f..."
...Deep neural networks have been studied for time series forecasting [8, 27, 34, 37], i....
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Cites methods from "Deep Learning: A Generic Approach f..."
...With convolutional neural network and recurrent neural network, stateof-the-art results are achieved in (Shi et al. 2015; Yu et al. 2017; Shi et al. 2017; Zhang, Zheng, and Qi 2017; Zhang et al. 2018a; Ma et al. 2017; Yao et al. 2018b; 2018a)....
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References
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"Deep Learning: A Generic Approach f..." refers background or methods in this paper
...When applied to accident forecasting, our architecture merges the outputs from the two components, instead of stacking them together....
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...We note interesting observations from the trained neural network....
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2,364 citations
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...3.2 Peak-hour Traffic Forecasting Peak-hour is the period when traffic congestion on roads hits its highest....
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2,306 citations
"Deep Learning: A Generic Approach f..." refers background in this paper
...For traffic forecasting, early attempts include deep belief network (DBN) [10], stacked autoencoder [15] and stacked denoising autoencoder [3]....
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1,587 citations
"Deep Learning: A Generic Approach f..." refers background in this paper
...The key to LSTMs is the cell state, which allows information to flow along the network....
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