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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 473432, 9 pages
http://dx.doi.org/10.1155/2015/473432
Research Article

An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

Received 22 July 2014; Accepted 9 October 2014

Academic Editor: Tao Chen

Copyright © 2015 Wen-jun Chu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Q. Meng and X. Qu, “Bus dwell time estimation at bus bays: a probabilistic approach,” Transportation Research Part C: Emerging Technologies, vol. 36, pp. 61–71, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. H. S. Levinson, “Transit travel time performance,” Transportation Research Record, vol. 915, pp. 1–6, 1983. View at Google Scholar · View at Scopus
  3. R. P. Guenthner and K. Hamat, “Transit dwell time under complex fare structure,” Journal of Transportation Engineering, vol. 114, no. 3, pp. 367–379, 1988. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Levine and G. Torng, “Dwell-time effects of low-floor bus design,” Journal of Transportation Engineering, vol. 120, no. 6, pp. 914–929, 1994. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Jaiswal, J. Bunker, and L. Ferreira, “Influence of platform walking on brt station bus dwell time estimation: Australian analysis,” Journal of Transportation Engineering, vol. 136, no. 12, pp. 1173–1179, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Tirachini, “Estimation of travel time and the benefits of upgrading the fare payment technology in urban bus services,” Transportation Research C: Emerging Technologies, vol. 30, pp. 239–256, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. J. G. Weston, “London underground train service model: a description of the model and its uses,” in Proceedings of the Computer Applications in Railway Planning and Management Conference (COMPRAIL '90), pp. 133–147, Rome, Italy, 1990.
  8. W. H. K. Lam, C.-Y. Cheung, and C. F. Lam, “A study of crowding effects at the Hong Kong light rail transit stations,” Transportation Research Part A: Policy and Practice, vol. 33, no. 5, pp. 401–415, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. T. M. Lin and N. H. M. Wilson, “Dwell time relationships for light rail systems,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1361, pp. 287–295, 1991. View at Google Scholar
  10. A. Puong, Dwell Time Model and Analysis for the MBTA Red Line, MIT OpenCourseWare, 2000, http://ocw.mit.edu/index.htm.
  11. M. T. Li, F. Zhao, L. F. Chow, H. Zhang, and S. C. Li, “Simulation model for estimating bus dwell time by simultaneously considering numbers of disembarking and boarding passengers,” Transportation Research Record, no. 1971, pp. 59–65, 2006. View at Google Scholar · View at Scopus
  12. Q. Zhang, B. Han, and D. Li, “Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations,” Transportation Research Part C: Emerging Technologies, vol. 16, no. 5, pp. 635–649, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Baee, F. Eshghi, S. M. Hashemi, and R. Moienfar, “Passenger boarding/alighting management in urban rail transportation,” in Proceedings of the Joint Rail Conference (JRC '12), pp. 823–829, Philadelphia, Pa, USA, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989. View at Publisher · View at Google Scholar · View at Scopus
  15. G. B. Huang, Learning capability of neural networks [Ph.D. thesis], Nanyang Technological University, Singapore, 1998.
  16. G.-B. Huang, Y.-Q. Chen, and H. A. Babri, “Classification ability of single hidden layer feedforward neural networks,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 799–801, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. G.-B. Huang and H. A. Babri, “Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions,” IEEE Transactions on Neural Networks, vol. 9, no. 1, pp. 224–229, 1998. View at Publisher · View at Google Scholar · View at Scopus
  18. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Rajesh and J. S. Prakash, “Extreme learning machines—a review and state-of-the-art,” International Journal of Wisdom Based Computing, vol. 1, no. 1, pp. 35–49, 2011. View at Google Scholar
  20. D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Physical Review E, vol. 51, no. 5, pp. 4282–4286, 1995. View at Publisher · View at Google Scholar · View at Scopus