The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
An efficient realization of deep learning for traffic data imputation
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
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
Self-citation
Cited by
DeepPF: A deep learning based architecture for metro passenger flow prediction
Liu, Y., Liu, Z., & Jia, R. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2019.01.027 · Cited by 426
Self-citationDeep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends
Veres, M., & Moussa, M. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2929020 · Cited by 394
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
Chen, X., He, Z., & Sun, L. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.11.003 · Cited by 373
Enhancing transportation systems via deep learning: A survey
Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 312
Self-citationA Survey on an Emerging Area: Deep Learning for Smart City Data
Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2907718 · Cited by 167
Metadata
- Authors
- Duan, Y., Lv, Y., Liu, Y.-L., & Wang, F.-Y.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 72
- Pages
- 168-181
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2529827714
- Cited by
- 316
- References
- 34
- FWCI
- 22.36
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: An efficient realization of deep learning for traffic data imputation. https://app.cochat.ai/papers/10.1016/j.trc.2016.09.015
- The paper
- Duan, Y., Lv, Y., Liu, Y.-L., & Wang, F.-Y. (2016). An efficient realization of deep learning for traffic data imputation. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2016.09.015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
