The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
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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
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1603.02754 · Cited by 7,104
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Cited by
Ali, A., Zhu, Y., & Zakarya, M. (2022) · Neural Networks · doi:10.1016/j.neunet.2021.10.021 · Cited by 432
Self-citationLin, G., Lin, A., & Gu, D. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.090 · Cited by 282
Mughaid, A., Obeidat, I., AlZu’bi, S., Elsoud, E. A., Alnajjar, A., Alsoud, A. R., & Abualigah, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14347-8 · Cited by 39
Metadata
- Authors
- Ali, A., Zhu, Y., & Zakarya, M.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 577
- Pages
- 852-870
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W3193349278
- Cited by
- 263
- References
- 5
- FWCI
- 17.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. https://app.cochat.ai/papers/10.1016/j.ins.2021.08.042
- The paper
- Ali, A., Zhu, Y., & Zakarya, M. (2021). Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Information Sciences. https://doi.org/10.1016/j.ins.2021.08.042
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.
