The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Detecting Novel Associations in Large Data Sets
Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., & Sabeti, P. C. (2011) · Science · doi:10.1126/science.1205438 · Cited by 3,339
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2935152 · Cited by 3,326
Comparison of parametric and nonparametric models for traffic flow forecasting
Smith, B. L., Williams, B. M., & Keith Oswald, R. (2002) · Transportation Research Part C: Emerging Technologies · doi:10.1016/s0968-090x(02)00009-8 · Cited by 1,006
Financial Forecasting Using Support Vector Machines
Cao, L., & Tay, F. E. H. (2001) · Neural Computing & Applications · doi:10.1007/s005210170010 · Cited by 392
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Peng, H., Wang, H., Du, B., Bhuiyan, M. Z. A., Ma, H., Liu, J., Wang, L., Yang, Z., Du, L., Wang, S., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2020.01.043 · Cited by 339
Ali, A., Zhu, Y., & Zakarya, M. (2021) · Information Sciences · doi:10.1016/j.ins.2021.08.042 · Cited by 263
Cited by
Bus Arrival Time Prediction: A Comprehensive Review
Kumar, B. A., Singh, R., Shaji, H. E., & Vanajakshi, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3545695 · Cited by 13
Metadata
- Authors
- Lin, G., Lin, A., & Gu, D.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 608
- Pages
- 517-531
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4283574791
- Cited by
- 282
- References
- 32
- FWCI
- 24.74
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient. https://app.cochat.ai/papers/10.1016/j.ins.2022.06.090
- The paper
- Lin, G., Lin, A., & Gu, D. (2022). Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient. Information Sciences. https://doi.org/10.1016/j.ins.2022.06.090
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.
