Journal Article10.1109/TITS.2014.2311123
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
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TL;DR: It is presented that MTL can improve the generalization performance of shared tasks and a grouping method based on the weights in the top layer to make MTL more effective is proposed to take full advantage of weight sharing in the deep architecture.
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Abstract: Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.
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Citations
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•Journal Article
Attention-based Recurrent Neural Network for Traffic Flow Prediction
TL;DR: Experimental results demonstrate that the proposed attention-based recurrent neural network architecture for multi-step traffic flow prediction has superior performance compared to the existing models and can be used to develop traffic anomaly detection systems.
Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction
01 Jun 2022
TL;DR: Zhang et al. as mentioned in this paper proposed an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction.
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Spatio-temporal fourier enhanced heterogeneous graph learning for traffic forecasting
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TL;DR: This paper introduces FEHGCARN, a novel traffic prediction model that integrates historical information and captures spatio-temporal dependencies using a graph convolution attention recurrent unit and fourier-enhanced heterogeneous graph learning.
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