The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification
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Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Mazloumi, E., Rose, G., Currie, G., & Moridpour, S. (2011) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2010.11.004 · Cited by 133
Cited by
Privacy-preserving blockchain-based federated learning for traffic flow prediction
Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.003 · Cited by 339
Survey on traffic prediction in smart cities
Nagy, A. M., & Simon, V. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.07.004 · Cited by 312
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
Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
Chen, X., Wu, S., Shi, C., Huang, Y., Yang, Y., Ke, R., & Zhao, J. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3007809 · Cited by 282
Boukerche, A., Tao, Y., & Sun, P. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107484 · Cited by 215
Metadata
- Authors
- Guo, J., Huang, W., & Williams, B. M.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 43
- Pages
- 50-64
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2049952439
- Cited by
- 643
- References
- 3
- FWCI
- 25.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. https://app.cochat.ai/papers/10.1016/j.trc.2014.02.006
- The paper
- Guo, J., Huang, W., & Williams, B. M. (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2014.02.006
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.
