Laplacian integration of graph convolutional network with tensor completion for traffic prediction with missing data in inter-city highway network
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DOI: 10.1016/j.physa.2021.126474
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- Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
- Yang, Senyan & Wu, Jianping & Xu, Yanyan & Yang, Tao, 2019. "Revealing heterogeneous spatiotemporal traffic flow patterns of urban road network via tensor decomposition-based clustering approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
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Cited by:
- Zhang, Ke & Lin, Xi & Li, Meng, 2023. "Graph attention reinforcement learning with flexible matching policies for multi-depot vehicle routing problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
- Wenbao Zeng & Ketong Wang & Jianghua Zhou & Rongjun Cheng, 2023. "Traffic Flow Prediction Based on Hybrid Deep Learning Models Considering Missing Data and Multiple Factors," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
- Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
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Keywords
Traffic prediction; Graph convolutional network; Missing data; Tensor completion; Graph Laplace;All these keywords.
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