Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2017, Article ID 3819304, 7 pages
Research Article

The Aggregation Mechanism Mining of Passengers’ Flow with Period Distribution Based on Suburban Rail Lines

College of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, China

Correspondence should be addressed to Xiaobing Ding; moc.361@adusbxd

Received 31 December 2016; Revised 21 March 2017; Accepted 8 May 2017; Published 22 June 2017

Academic Editor: Seenith Sivasundaram

Copyright © 2017 Xiaobing Ding 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. W. Gordon, “Improved modeling of non-home-base trips,” Transportation Research Record, pp. 23–25, 1996. View at Google Scholar
  2. R. C. M. Yam, R. C. Whitfield, and R. W. F. Chung, “Forecasting traffic generation in public housing estates,” Journal of Transportation Engineering, vol. 126, no. 4, pp. 358–361, 2000. View at Google Scholar
  3. J. L. Bowman and M. E. Ben-Akiva, “Activity-based disaggregate travel demand model system with activity schedules,” Transportation Research A: Policy and Practice, vol. 35, no. 1, pp. 1–28, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Xiaoguang, “Multinomial distribution lagging model used for short-term traffic flow prediction,” Journal of Tongji University, vol. 39, no. 9, pp. 1297–1302, 2011. View at Google Scholar
  5. Y. Enjian, Study on Urban Rail Transit OD Distribution Prediction Models under Networking Condition [D], Beijing Jiaotong University, 2013.
  6. Z. Peng, “Urban rail transit network short-term passenger flow OD estimation model,” The Journal of Traffic Systems Engineering and Information Technology, vol. 15, no. 2, pp. 149–155, 2015. View at Google Scholar
  7. C. Xiaohong, “Study on rail transit passenger flow dynamic OD matrix estimation methods,” The Journal of Tongji University, vol. 24, no. 12, pp. 56–72, 2010. View at Google Scholar
  8. A. Jamili, M. A. Shafia, S. J. Sadjadi, and R. Tavakkoli-Moghaddam, “Solving a periodic single-track train timetabling problem by an efficient hybrid algorithm,” Engineering Applications of Artificial Intelligence, vol. 25, no. 4, pp. 793–800, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. M. T. Isaai, A. Kanani, M. Tootoonchi, and H. R. Afzali, “Intelligent timetable evaluation using fuzzy AHP,” Expert Systems with Applications, vol. 38, no. 4, pp. 3718–3723, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Ceder, “Public-transport automated timetables using even headway and even passenger load concepts,” in Proceedings of the 32nd Australasian Transport Research Forum (ATRF '09), 17, p. 1, Auckland, New Zealand, October 2009. View at Scopus
  11. A. Nuzzolo, U. Crisalli, and L. Rosati, “A schedule-based assignment model with explicit capacity constraints for congested transit networks,” Transportation Research Part C: Emerging Technologies, vol. 20, no. 1, pp. 16–33, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Carey and I. Crawford, “Scheduling trains on a network of busy complex stations,” Transportation Research B: Methodological, vol. 41, no. 2, pp. 159–178, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. V. Guihaire and J. K. Hao, “Transit network design and scheduling: a global review,” Transportation Research A: Policy and Practice, vol. 42, no. 10, pp. 1251–1273, 2008. View at Publisher · View at Google Scholar · View at Scopus