English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 55025/89277 (62%)
造访人次 : 10605034      在线人数 : 26
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/68719

    题名: Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems
    作者: Wen, Yuh-horng;Lee, Tsu-tian;Lee, Tsu-tian
    贡献者: 淡江大學運輸管理學系
    日期: 2005-08
    上传时间: 2011-10-23 13:42:12 (UTC+8)
    出版者: IEEE Systems, Man, and Cybernetics Society
    摘要: This study presents a systematic process combining trajfic forecasting and data mining models for traffic information systems. Fuzzy c-means clustering model was developed for mining traffic flow-speed-occupancy relationships, then to extrapolate traffic information. The hybrid grey-based recurrent neural network (G-RNN) was developed for traffic parameter forecasting. G-RNN integrates grey modeling into recurrent neural networks that is capable of dealing with both randomness and spatial-temporal properties in trajfic data implicitly. Field data from Taiwan national freeway was used as an example for testing the proposed models. Study results were shown that the G-RNN model is capable of predicting traffic parameters with a high degree of accuracy. The application presents three clusters built from data and recognized three types of traffic conditions. Study results also showed feasibility of the method for advanced traffic information systems.
    關聯: 2005 IEEE International Conference on Information Reuse and Integration, p.p 356-361
    显示于类别:[運輸管理學系暨研究所] 會議論文





    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈