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In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE).
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various existing deep learning architectures used for...
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9 de sept. de 2014 · In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion.
10 de feb. de 2021 · Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions Abstract: Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion.
1 de sept. de 2023 · Traffic flow prediction is important for urban planning and traffic congestion alleviation as well as for intelligent traffic management systems. Due to the periodic characteristics and high fluctuation in short-term periods, it is difficult to accurately estimate future patterns in traffic flow on the urban road network.
13 de sept. de 2022 · This paper proposed a traffic flow prediction model based on a deep learning framework, the FASTNN, which can model ST aggregation and quantify intrinsic correlation and redundancy of ST features.
9 de mar. de 2023 · Artificial intelligence-based traffic flow prediction: a comprehensive review. Sayed A. Sayed, Yasser Abdel-Hamid & Hesham Ahmed Hefny. Journal of Electrical Systems and Information Technology 10, Article number: 13 ( 2023 ) Cite this article. 9908 Accesses. 5 Citations. Metrics. Abstract.
16 de jul. de 2023 · Traffic Flow Prediction Based on Hybrid Deep Learning Models Considering Missing Data and Multiple Factors. by. Wenbao Zeng. 1,2,3, Ketong Wang. 1,2,3, Jianghua Zhou. 1,2,3 and. Rongjun Cheng. 1,2,3,* Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China. 2.
