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基于組合模型的短時(shí)交通流量預(yù)測算法

芮蘭蘭 李欽銘

芮蘭蘭, 李欽銘. 基于組合模型的短時(shí)交通流量預(yù)測算法[J]. 電子與信息學(xué)報(bào), 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846
引用本文: 芮蘭蘭, 李欽銘. 基于組合模型的短時(shí)交通流量預(yù)測算法[J]. 電子與信息學(xué)報(bào), 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846
RUI Lanlan, LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846
Citation: RUI Lanlan, LI Qinming. Short-term Traffic Flow Prediction Algorithm Based on Combined Model[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1227-1233. doi: 10.11999/JEIT150846

基于組合模型的短時(shí)交通流量預(yù)測算法

doi: 10.11999/JEIT150846
基金項(xiàng)目: 

國家自然科學(xué)基金創(chuàng)新研究群體科學(xué)基金(61121061),國家自然科學(xué)基金(61302078, 61372108),北京高等學(xué)校青年英才計(jì)劃項(xiàng)目(YETP0476)

Short-term Traffic Flow Prediction Algorithm Based on Combined Model

Funds: 

Funds for Creative Research Groups of China (61121061), The National Natural Science Foundation of China (61302078, 61372108), Beijing Higher Education Young Elite Teacher Project (YETP0476)

  • 摘要: 交通流量預(yù)測是實(shí)現(xiàn)智能交通技術(shù)的核心問題,及時(shí)準(zhǔn)確地預(yù)測道路交通流量是實(shí)現(xiàn)動(dòng)態(tài)交通管理的前提,短時(shí)交通流量的預(yù)測是交通流量預(yù)測的重要組成部分。該文針對十字路口的短時(shí)交通流量預(yù)測問題設(shè)計(jì)了基于交通流量序列分割和極限學(xué)習(xí)機(jī)(Extreme Learning Machine, ELM)組合模型的交通流量預(yù)測算法(Traffic Flow Prediction Based on Combined Model, TFPBCM)。該算法首先采用K-means對交通流量數(shù)據(jù)在時(shí)間上進(jìn)行序列分割,然后采用ELM對各個(gè)序列進(jìn)行建模和預(yù)測。仿真實(shí)驗(yàn)證明,與單一的BP(Back Propagation)神經(jīng)網(wǎng)絡(luò)和ELM相比,該組合模型算法建模時(shí)間為BP的1/10, ELM建模時(shí)間的4倍,均方誤差為BP的1/50, ELM的1/20,該組合模型算法決定系數(shù)R2更接近于1,模型可信度更高。
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出版歷程
  • 收稿日期:  2015-07-14
  • 修回日期:  2016-01-08
  • 刊出日期:  2016-05-19

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