Short-Term travel speed prediction for urban expressways using convolutional neural network and tensor decomposition

Author:

Han Tianyang,Tang Keshuang,Oguchi Takashi

Publisher

Elsevier BV

Reference25 articles.

1. On optimal low rank tucker approximation for tensors: the case for an adjustable core size;Chen;Journal of Global Optimization,2015

2. Chen, C, Liu, X, Qiu, T, Sangaiah, A.K, 2017. A short-term traffic prediction model in the vehicular cyber–physical systems. Future Generation Computer Systems URL: http://www.sciencedirect.com/science/article/pii/S0167739X17311846, doi:https://doi.org/10.1016/j.future.2017.06.006.

3. A bayesian tensor decomposition approach for spatiotemporal traffic data imputation;Chen;Transportation Research Part C: Emerging Technologies,2019

4. Spatial-temporal traffic speed patterns discovery and incomplete data recovery via svd-combined tensor decomposition;Chen;Transportation Research Part C: Emerging Technologies,2018

5. Duan, Y, Lv, Y, Wang, F, 2016. Performance evaluation of the deep learning approach for traffic flow prediction at different times, in: 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 223–227. doi:10.1109/SOLI.2016. 7551691.

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