Abstract
Short-term travel flow prediction has been the core of the intelligent transport systems (ITS). An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function, this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
ZHU T, KONG X, LV W. Large-scale travel time prediction for urban arterial roads based on Kalman filter [C]// 2009 International Conference on Computational Intelligence and Software Engineering, Wuhan, China: ICCISE, 2009:1–5. doi: 10.1109/CISE.2009.5365441
LI L, WANG D, XIAO Z, LI X. Urban road travel time prediction based on ELM [C]// International Conference on Advanced Materials and Computer Science. Qingdao, China: ICAMCS, 2016: 392–395. doi:10.2991/icamcs-16.2016.83
HUGAN G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications [J]. Neurocomputing, 2006, 70(1-3): 489–501.
DING X J, CHANG B F. Active set strategy of optimized extreme learning machine [J]. Science Bulletin, 2014, 59(31): 4152–4160.
FENG L, WANG J, LIU S, XIAO Y. Multi-label dimensionality reduction and classification with extreme learning machines [J]. Journal of Systems Engineering and Electronics, 2014, 25(3): 502–513.
LI B J, CHENG C T. Monthly discharge forecasting using wavelet neural networks with extreme learning machine [J]. Science China Technological Sciences, 2014, 57(12): 2441–2452.
HAN M, ZHANG R, XU M. Multivariate chaotic time series prediction based on ELM–PLSR and hybrid variable selection algorithm [J]. Neural Processing Letters, 2017: 1–13.
PAN F, ZHAO H B. Online sequential extreme learning machine based multilayer perception with output self feedback for time series prediction [J]. Journal of Shanghai Jiaotong University (Science), 2013, 18(3): 366–375.
BEZDEK J C, EHRLICH R, FULL W. FCM: The fuzzy C-means clustering algorithm [J]. Computers & Geosciences, 1984, 10(2, 3): 191–203.
HUANG G B. What are extreme learning machines? filling the gap between frank Rosenblatt’s dream and John von Neumann’s puzzle [J]. Cognitive Computation, 2015, 7(3): 263–278.
DENG C W, HUNAG G B, XU J, TANG JX. Extreme learning machines: new trends and applications [J]. Science China Information Sciences, 2015, 58(2): 1–16.
XU Long-qin, Combined prediction model of water temperature in industrialized cultivation based on empirical mode decomposition and extreme learning machine [J]. Journal of Agricultural Mechanization, 2016, 47(4): 265–271.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Project(2016YFB0100906) supported by the National Key R & D Program in China; Project(2014BAG03B01) supported by the National Science and Technology Support plan Project China; Project(61673232) supported by the National Natural Science Foundation of China; Projects(DlS11090028000, D171100006417003) supported by Beijing Municipal Science and Technology Program, China
Rights and permissions
About this article
Cite this article
Wang, Xc., Hu, Jm., Liang, W. et al. Short-term travel flow prediction method based on FCM-clustering and ELM. J. Cent. South Univ. 24, 1344–1350 (2017). https://doi.org/10.1007/s11771-017-3538-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-017-3538-1