The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Travel-Time Prediction With Support Vector Regression
Abstract
Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
SVM-based detection of moving vehicles for automatic traffic monitoring
Gao, D., Zhou, J., & Xin, L. (2002) · ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585) · doi:10.1109/itsc.2001.948753 · Cited by 34
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
Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2020) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2020.102674 · Cited by 486
Travel time prediction with LSTM neural network
Duan, Y., L.V., Y., & Wang, F.-Y. (2016) · 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795686 · Cited by 336
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Machine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
Metadata
- Authors
- Wu, C.-H., Ho, J.-M., & Lee, D. T.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2004-12
- Volume
- 5
- Issue
- 4
- Pages
- 276-281
- ISSN
- 1524-9050 (ISSN-L)
- OpenAlex
- W2069929199
- Cited by
- 1,444
- References
- 2
- FWCI
- 66.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wu, C.-H., Ho, J.-M., & Lee, D. T. (2004). Travel-Time Prediction With Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2004.837813
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