The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 5,839
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
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Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000
Cited by
Gupta, S., S., R., K., R., & Verma, R. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110941 · Cited by 26
FedTSE: Low-Cost Federated Learning for Privacy-Preserved Traffic State Estimation in IoV
Yuan, X., Chen, J., Zhang, N., Zhu, C., Ye, Q., & Shen, X. S. (2022) · IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798018 · Cited by 22
Ardeshiri, R. R., & Ma, C. (2021) · 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576245 · Cited by 17
Kumar, D., Rizwan, M., & Panwar, A. K. (2023) · 2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE) · doi:10.1109/pesgre58662.2023.10404810 · Cited by 9
Metadata
- Authors
- Cui, Z., Ke, R., Pu, Z., & Wang, Y.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 118
- Pages
- 102674
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W3041279471
- Cited by
- 486
- References
- 46
- FWCI
- 32.15
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2020). Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2020.102674
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
