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All issues Volume 218 (2018) MATEC Web Conf., 218 (2018) 03021 References
Open Access
Issue
MATEC Web Conf.
Volume 218, 2018
The 1st International Conference on Industrial, Electrical and Electronics (ICIEE 2018)
Article Number 03021
Number of page(s) 6
Section Information Technology
DOI https://doi.org/10.1051/matecconf/201821803021
Published online 26 October 2018
  1. N. Petrovska and A. Stevanovic, “Traffic Congestion Analysis Visualisation Tool,” IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC, vol. 2015–Octob, pp. 1489–1494, (2015). [Google Scholar]
  2. G. Zhu, K. Song, P. Zhang, and L. Wang, “A traffic flow state transition model for urban road network based on Hidden Markov Model,” Neurocomputing, vol. 214, pp. 567–574, (2016). [CrossRef] [Google Scholar]
  3. H. Dong, L. Jia, X. Sun, C. Li, and Y. Qin, “Road traffic flow prediction with a time-oriented ARIMA model,” in NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC, no. 1, pp. 1649–1652 (2009). [CrossRef] [Google Scholar]
  4. E. E. E. Lee, J. J. J. Kim, and W. W. Yoon, “Traffic speed prediction under weekday, time, and neighboring links’ speed: Back propagation neural network approach,” in … Theories and Applications. With Aspects of …, no. Mic, pp. 626–635 (2007). [Google Scholar]
  5. K. Kumar, M. Parida, and V. K. Katiyar, “Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network,” in Procedia - Social and Behavioral Sciences, vol. 104, pp. 755–764 (2007). [CrossRef] [Google Scholar]
  6. J. Kim and G. Wang, “Diagnosis and Prediction of Traffic Congestion on Urban Road Networks Using Bayesian Networks,” in Australasian Transport Research Forum 2016, no. 2595, pp. 1–21 (2016). [Google Scholar]
  7. S. S. Aung and T. T. Naing, “Naïve Bayes Classifier Based Traffic Detection System on Cloud Infrastructure,” 2015 6th Int. Conf. Intell. Syst. Model. Simul., pp. 193–198, (2015). [Google Scholar]
  8. M. D. Kindzerske and D. Ni, “Composite Nearest Neighbor Nonparametric Regression to Improve Traffic Prediction,” Transp. Res. Rec., no. 1993, p. pp 30-35, (2007). [CrossRef] [Google Scholar]
  9. B. Yu, X. Song, F. Guan, Z. Yang, and B. Yao, “k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition,” J. Transp. Eng., vol. 142, no. 6, p. 04016018 (2016). [CrossRef] [Google Scholar]
  10. X. Pang, C. Wang, and G. Huang, “A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm,” J. Transp. Technol., vol. 6, no. 4, pp. 200–206 (2016). [CrossRef] [Google Scholar]
  11. Y. Wu, H. Tan, P. Jin, B. Shen, and B. Ran, “Short-Term Traffic Flow Prediction Based on Multilinear Analysis and k-Nearest Neighbor Regression,” 15th Int. Conf. Transp. Prof., pp. 557–569 (2015). [Google Scholar]
  12. L. Zhang, Q. Liu, W. Yang, N. Wei, and D. Dong, “An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction,” Procedia - Soc. Behav. Sci., vol. 96, no. Cictp, pp. 653–662 (2013). [CrossRef] [Google Scholar]
  13. B. Priambodo and A. Ahmad, “Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression BT - Advances in Visual Informatics: 5th International Visual Informatics Conference, IVIC 2017, Bangi, Malaysia, November 28–30, 2017, Proceedings,” pp. 309–318 (2017). [Google Scholar]
  14. K. Lee, B. Hong, D. Jeong, and J. Lee, “Congestion pattern model for predicting short-term traffic decongestion times,” in 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, pp. 2828–2833 (2014). [Google Scholar]
  15. F. Rempe, G. Huber, and K. Bogenberger, “Spatio-Temporal Congestion Patterns in Urban Traffic Networks,” Transp. Res. Procedia, vol. 15, pp. 513–524 (2016). [CrossRef] [Google Scholar]
  16. G. Wang and J. Kim, “Prediction of Traffic Congestion and Incidents on Urban Road Networks Using Naive Bayes Classifier,” no. February (2017, 2016). [Google Scholar]
  17. S. Bischof, A. Karapantelakis, A. Sheth, A. Mileo, and P. Barnaghi, “Semantic Modelling of Smart City Data,” in W3C Workshop on the Web of Things Enablers and services for an open Web of Devices, pp. 1–5 (2014). [Google Scholar]

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