The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory
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Citations
Cites
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,429
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Daubechies, I., & Heil, C. (1992) · Computers in Physics · doi:10.1063/1.4823127 · Cited by 15,631
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
Ideal spatial adaptation by wavelet shrinkage
Donoho, D. L., & Johnstone, I. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.425 · Cited by 8,760
Orthonormal bases of compactly supported wavelets
Daubechies, I. (1988) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.3160410705 · Cited by 8,190
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,789
Measuring the strangeness of strange attractors
Grassberger, P., & Procaccia, I. (1983) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(83)90298-1 · Cited by 5,650
Adapting to Unknown Smoothness via Wavelet Shrinkage
Donoho, D. L., & Johnstone, I. M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476626 · Cited by 4,630
Independent coordinates for strange attractors from mutual information
Fraser, A. M., & Swinney, H. L. (1986) · Physical Review A · doi:10.1103/physreva.33.1134 · Cited by 4,606
Cited by
Short-term traffic forecasting: where we are and where we’re going
Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2014.01.005 · Cited by 1,132
A gradient boosting method to improve travel time prediction
Zhang, Y., & Haghani, A. (2015) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2015.02.019 · Cited by 832
Short-term traffic forecasting: an LSTM network for spatial-temporal speed prediction
Abduljabbar, R., Dia, H., Tsai, P.-W., & Liyanage, S. (2021) · Future Transportation · doi:10.3390/futuretransp1010003 · Cited by 49
Metadata
- Authors
- Wang, J., & Shi, Q.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 27
- Pages
- 219-232
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2047493229
- Cited by
- 289
- References
- 35
- FWCI
- 15.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory. https://app.cochat.ai/papers/10.1016/j.trc.2012.08.004
- The paper
- Wang, J., & Shi, Q. (2013). Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2012.08.004
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.
