期刊文献+

基于灰色关联度和滤波方法的交通流周期特性研究

下载PDF
导出
摘要 本文利用灰色关联度计算了交通流时间序列的周期性特点,并利用滤波方法分析了交通流的波动性特性。灰色关联度数据表明交通流量具有显著的周期特性,且以一周(7d)为周期,星期数对应的数据具有较高的相关性。
作者 杨婷
出处 《建材与装饰》 2017年第25期236-238,共3页 Construction Materials & Decoration
  • 相关文献

参考文献1

二级参考文献11

  • 1郑为中,史其信.基于贝叶斯组合模型的短期交通量预测研究[J].中国公路学报,2005,18(1):85-89. 被引量:47
  • 2Okutani I, Stephanedes Y J. Dynamic prediction of traffic volume through Kalman filtering theory [ J ]. Transportation Research Part B: Methodological,1984,18(1) : 1 -11.
  • 3Castro-Neto M,Jeong Y-S,Jeong M-K,et al. Online-SVR for short-te~ traffic flow prediction under typical and atypical traffic conditions [ J ]. Expert Systems with Applications, 200c)~36(3 ) :6164 - 6173.
  • 4Hong W C, Dong Y C, Zheng F F, et al. Forecasting urban traffic flow by SVR with continuous ACO [ J ]. Applied Math- ematical Modelling,2011,35 (3) : 1282 - 1291.
  • 5Vlahogianni E I, Karlaftis M G, Golias J C. Optimized and meta-optimized neural networks for short-term traffic flow prediction : A genetic approach[ J]. Transportation Research Part C : Emerging Technologies ,2005,13 (3) :211 - 234.
  • 6Lee S, Fambro D B. Application of subset autoregressive in- tegrated moving average model for short-term freeway traffic volume forecasting [ C ]//Transportation Research Record. Washington DC, 1999 : 179 - 188.
  • 7Lu Jianchang, Niu Dongxiao, Jia Zhengyuan. A study of short-term load forecasting based on ARIMA-ANN [ J ]. Ma- chine Learning and Cybernetics,2004 (5) : 3183 - 3187.
  • 8William B M. Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process [ D ]. Charlottes- ville : University of Virginia, 1999.
  • 9Williams B M, Hoel L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process:A theoretical basis and empirical results [ J ]. Journal of Transportation Engi- neering,2003,129(6) :664 - 672.
  • 10薛洁妮,史忠科.基于混沌时间序列分析法的短时交通流预测研究[J].交通运输系统工程与信息,2008,8(5):68-72. 被引量:47

共引文献32

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部