Adaptive data processing framework for efficient short-term traffic flow prediction
Author:
Funder
Key Technologies Research and Development Program
the National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11071-024-09844-w.pdf
Reference55 articles.
1. Jeong, Y.S., Byon, Y.J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 14(4), 1700–1707 (2013). https://doi.org/10.1109/TITS.2013.2267735
2. Castro-Neto, M., Jeong, Y.S., Jeong, M.K., Han, L.D.: Online-svr for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36(3), 6164–6173 (2009). https://doi.org/10.1016/j.eswa.2008.07.069
3. Liu, L., Zhen, J., Li, G., Zhan, G., He, Z., Du, B., Lin, L.: Dynamic spatial-temporal representation learning for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 22(11), 7169–7183 (2020). https://doi.org/10.1109/TITS.2020.3002718
4. Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J.: Lstm network: a deep learning approach for short-term traffic forecast. IET Intel. Transp. Syst. 11(2), 68–75 (2017). https://doi.org/10.1049/iet-its.2016.0208
5. Miri, F., Namanloo, A.A., Pazzi, R., Martin, M.V.: A novel short-term post-accident traffic prediction model. In: 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 189–196. IEEE (2021). https://doi.org/10.1109/DCOSS52077.2021.00041
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