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All issues Volume 308 (2020) MATEC Web Conf., 308 (2020) 02005 References
Open Access
Issue
MATEC Web Conf.
Volume 308, 2020
2019 8th International Conference on Transportation and Traffic Engineering (ICTTE 2019)
Article Number 02005
Number of page(s) 5
Section Urban Rail and Traffic Patterns
DOI https://doi.org/10.1051/matecconf/202030802005
Published online 12 February 2020
  1. R. More, A. Mugal, S. Rajgure, et al. Road traffic prediction and congestion control using artificial neural networks. International Conference on Computing, Analytics and Security. IEEE. 52-57, (2016). [Google Scholar]
  2. U. Mori, A. Mendiburu, M. álvarez, et al. A review of travel time estimation and forecasting for Advanced hraveller Information Systems. hransportmetrica A: hransport Science. 11(2):119-157, (2015). [Google Scholar]
  3. A. AllstrM, M.J. Ekstr, R.D. Gundleg, et al. Hybrid Approach for Short-herm hraffic State and hravel hime Prediction on Highways. hransportation Research Record: Journal of the hransportation Research Board. 2554:60-68, (2016). [CrossRef] [Google Scholar]
  4. H.B. Celikoglu. Flow-based freeway travel-time estimation: A comparative evaluation within dynamic path loading. IEEE hransactions on Intelligent hransportation Systems. 14(2):772-781, (2013). [CrossRef] [Google Scholar]
  5. R.X. Zhong, J.C. Luo, et al. An Online hravel hime Prediction Model Based on Functional Data Analysis. hransportation Research Board 96th Annual Meeting. (2017). [Google Scholar]
  6. S. hak, S. Kim, K. Jang, et al. Real-hime hravel hime Prediction Using Multi-Level k-Nearest Neighbor Algorithm and Data Fusion Method. 2014 International Conference on Computing in Civil and Building Engineering. (2014). [Google Scholar]
  7. Z.Y. Lu, Q. Meng, G. Gomes, et al. Estimating link travel time functions for heterogeneous traffic flows on freeways. Journal of Advanced hransportation. 50(8), 1683-1698, (2016). [CrossRef] [Google Scholar]
  8. J.N. Chen, X. Zhang, S.R. Zhang. Analysis and empirical study of highway travel time interval prediction based on Bootstrap-KNN. Control and Decision. 33(11):2080-2086, (2018). [Google Scholar]
  9. X. Wang, X.H. Chen, X.M. Yang. Short-term travel time prediction of expressway based on K nearest neighbor algorithm. China Journal of Highway and hransport. 28(01), 102-111, (2015). [Google Scholar]
  10. X. Zhi, W.J. Chen, L.L. Li. A method based on interval-valued fuzzy soft set for multi-attribute group decision-making problems under uncertain environment. Knowledge and Information Systems. 34(03), 653–669, (2013). [CrossRef] [Google Scholar]
  11. X. Zhi, K. Gong, Y. Zou. A combined forecasting approach based on fuzzy soft sets. Journal of Computational and Applied Mathematics. 228 (1):326–333, (2009). [CrossRef] [Google Scholar]
  12. Y.C. Shao. An application of fuzzy rough sets in predicting on urban traffic congestion. 14th International Symposium on Distributed Computing & Applications for Business, Engineering and Science. IEEE. 208-211, (2016). [Google Scholar]
  13. D. Molodtsov. Soft set theory–first results. Computers and Mathematics with Applications. 37(4-5), 19–31, (1999). [CrossRef] [Google Scholar]
  14. P.K. Maji, R. Biswas, A.R. Roy. Soft set theory. Computers and Mathematics with Applications. 45(4-5), 555-562, (2003). [CrossRef] [Google Scholar]
  15. X. Zhi, K. Gong, et al. Exclusive disjunctive soft sets. Computers & Mathematics with Applications. 59(6), 2128-2137, (2010). [CrossRef] [Google Scholar]
  16. S. Danjuma, M.A. Ismail, h. Herawan. An alternative approach to normal parameter reduction algorithm for soft set theory. IEEE Access.5, 4732-4746, (2017). [Google Scholar]
  17. K. Gong, P.P. Wang, X. Zhi. Bijective soft set decision system based parameters reduction under fuzzy environments. Applied Mathematical Modelling. 37(6), 4474-4485, (2013). [Google Scholar]
  18. R. Li, C. Jiang, F. Zhu, et al. hraffic Flow Data Forecasting Based on Interval hype-2 Fuzzy Sets hheory. IEEE/CAA Journal of Automatica Sinica. 3(2), 141-148, (2016). [Google Scholar]
  19. H.L. Yang, Z.L. Guo. Kernels and closures of soft set relations, and soft set relation mappings. Computers and Mathematics with Applications. 61(3), 651-662, (2011). [CrossRef] [Google Scholar]

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