DOI QR코드

DOI QR Code

A New Approach for Hierarchical Dividing to Passenger Nodes in Passenger Dedicated Line

  • Zhao, Chanchan (School of Computer and Information Technology, Beijing Jiaotong University) ;
  • Liu, Feng (School of Computer and Information Technology, Beijing Jiaotong University) ;
  • Hai, Xiaowei (School of Economics and Management, Inner Mongolia University of Technology)
  • 투고 : 2017.02.22
  • 심사 : 2017.06.21
  • 발행 : 2018.06.30

초록

China possesses a passenger dedicated line system of large scale, passenger flow intensity with uneven distribution, and passenger nodes with complicated relations. Consequently, the significance of passenger nodes shall be considered and the dissimilarity of passenger nodes shall be analyzed in compiling passenger train operation and conducting transportation allocation. For this purpose, the passenger nodes need to be hierarchically divided. Targeting at problems such as hierarchical dividing process vulnerable to subjective factors and local optimum in the current research, we propose a clustering approach based on self-organizing map (SOM) and k-means, and then, harnessing the new approach, hierarchical dividing of passenger dedicated line passenger nodes is effectuated. Specifically, objective passenger nodes parameters are selected and SOM is used to give a preliminary passenger nodes clustering firstly; secondly, Davies-Bouldin index is used to determine the number of clusters of the passenger nodes; and thirdly, k-means is used to conduct accurate clustering, thus getting the hierarchical dividing of passenger nodes. Through example analysis, the feasibility and rationality of the algorithm was proved.

키워드

참고문헌

  1. ChinaDaily, "Medium- and long-term railway network plan," 2016 [Online]. Available: http://www.chinadaily.com.cn/opinion/2016-07/01/content_25925793.htm.
  2. W. X. Wang and H. X. Lyu, "Classification of railway passenger transport nodes based on affinity propagation cluster," Application Research of Computers, vol. 33, no. 10, pp. 2926-2928, 2016.
  3. B. Gao, Y. Qin, X. M. Xiao and L. X. Zhu, "K-means clustering analysis of key nodes and edges in Beijing subway network," Journal of Transportation Systems Engineering and Information Technology, vol. 14, no. 3, pp. 207-213, 2014.
  4. Y. Z. Xu and Y. Qin, "Factor analysis of key nodes in urban rail network," in Proceedings of IEEE International Conference on Intelligent Transportation Engineering, Singapore, 2016, pp. 27-31.
  5. P. F. Zhou, B. M. Han, and Q. Zhang, "High-speed railway passenger node classification method and train stops scheme," Applied Mechanics and Materials, vol. 505-506, pp. 632-636, 2014. https://doi.org/10.4028/www.scientific.net/AMM.505-506.632
  6. J. S. Park and K. Lee, "Classification of the Seoul Metropolitan Subway Stations using graph partitioning," Journal of the Economic Geographical Society of Korea, vol. 15, no. 3, pp. 343-357, 2012. https://doi.org/10.23841/egsk.2012.15.3.343
  7. T. Kohonen, "Self-organizing map," Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990. https://doi.org/10.1109/5.58325
  8. F. Wang, B. L. Xu, Y. W. Qian, Y. M. Dai and Z. Q. Wang, "Anomaly Detection Model Based on Hybrid Classifiers," Journal of System Simulation, vol. 24, no. 2, pp. 854-858, Feb. 2012.
  9. Y. H. Jin, A. Kawamura, S. C. Park, N. Nakagawa, H. Amaguchi, and J. Olsson, "Spatiotemporal classification of environmental monitoring data in the Yeongsan River basin, Korea, using self-organizing maps," Journal of Environmental Monitoring, vol. 13, no. 10, pp. 2886-2894, 2011. https://doi.org/10.1039/c1em10132c
  10. M. Alvarez-Guerra, C. Gonzalez-Pinuela, A. Andres, B. Galan, and J. R. Viguri, "Assessment of self-organizing map artificial neural networks for the classification of sediment quality," Environment International, vol. 34, no. 6, pp. 782-790, 2008. https://doi.org/10.1016/j.envint.2008.01.006
  11. K. Nishiyama, S. Endo, K. Jinno, C. B. Uvo, J. Olsson, and R. Berndtsson, "Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a self-organizing map," Atmospheric Research, vol. 83, no. 2-4, pp. 185-200, 2007. https://doi.org/10.1016/j.atmosres.2005.10.015
  12. V. S. Lobo, "Application of self-organizing maps to the maritime environment," in Information Fusion and Geographical Information Systems. Heidelberg: Springer, 2009, pp. 19-36.
  13. M. Liukkonen, E. Havia, H. Leinonen, and Y. Hiltunen, "Quality-oriented optimization of wave soldering process by using self-organizing maps," Applied Soft Computing, vol. 11, no. 1, pp. 214-220, 2011. https://doi.org/10.1016/j.asoc.2009.11.011
  14. J. C. Creput, A. Hajjam, A. Koukam, and O. Kuhn, "Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem," Journal of Combinatorial Optimization, vol. 24, no. 4, pp. 437-458, 2012. https://doi.org/10.1007/s10878-011-9400-8
  15. J. B. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, 1967, pp. 281-297.
  16. L. Zhang, M. Scholz, A. Mustafa, and R. Harrington, "Application of the self-organizing map as a prediction tool for an integrated constructed wetland agroecosystem treating agricultural runoff," Bioresource Technology, vol. 100, no. 2, pp. 559-565, 2009. https://doi.org/10.1016/j.biortech.2008.06.042
  17. D. Bedoya, V. Novotny, and E. S. Manolakos, "Instream and offstream environmental conditions and stream biotic integrity: importance of scale and site similarities for learning and prediction," Ecological Modelling, vol. 220, no. 19, pp. 2393-2406, 2009. https://doi.org/10.1016/j.ecolmodel.2009.06.017
  18. S. Greco, R. Slowinski, and I. Szczech, "Properties of rule interestingness measures and alternative approaches to normalization of measures," Information Sciences, vol. 216, pp. 1-16, 2012. https://doi.org/10.1016/j.ins.2012.05.018
  19. H. L. Garcia and I. M. Gonzalez, "Self-organizing map and clustering for wastewater treatment monitoring," Engineering Applications of Artificial Intelligence, vol. 17, no. 3, pp. 215-225, 2004. https://doi.org/10.1016/j.engappai.2004.03.004
  20. D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224-227, 1979.
  21. A. Hentati, A. Kawamura, H. Amaguchi, and Y. Iseri, "Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the self-organizing map," Geomorphology, vol. 122, no. 1-2, pp. 56-64, 2010. https://doi.org/10.1016/j.geomorph.2010.05.013