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中南大学学报(英文版)

Journal of Central South University

Vol. 24    No. 6    June 2017

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Short-term travel flow prediction method based on FCM-clustering and ELM
WANG Xing-chao(王星超)1, HU Jian-ming(胡坚明)2, LIANG Wei(梁伟)2, ZHANG Yi(张毅)2

1. Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;
2. Department of Automation, Tsinghua University, Beijing 100084, China

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.

 

Key words: intelligent transportation systems (ITS); travel flow prediction; extreme learning machine (ELM); FCM-clustering; spatio-temporal relation

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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