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基于模糊C均值聚類(lèi)和隨機(jī)森林的短時(shí)交通狀態(tài)預(yù)測(cè)方法

陳忠輝 凌獻(xiàn)堯 馮心欣 鄭海峰 徐藝文

陳忠輝, 凌獻(xiàn)堯, 馮心欣, 鄭海峰, 徐藝文. 基于模糊C均值聚類(lèi)和隨機(jī)森林的短時(shí)交通狀態(tài)預(yù)測(cè)方法[J]. 電子與信息學(xué)報(bào), 2018, 40(8): 1879-1886. doi: 10.11999/JEIT171090
引用本文: 陳忠輝, 凌獻(xiàn)堯, 馮心欣, 鄭海峰, 徐藝文. 基于模糊C均值聚類(lèi)和隨機(jī)森林的短時(shí)交通狀態(tài)預(yù)測(cè)方法[J]. 電子與信息學(xué)報(bào), 2018, 40(8): 1879-1886. doi: 10.11999/JEIT171090
CHEN Zhonghui, LING Xianyao, FENG Xinxin, ZHENG Haifeng, XU Yiwen. Short-term Traffic State Prediction Approach Based on FCM and Random Forest[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1879-1886. doi: 10.11999/JEIT171090
Citation: CHEN Zhonghui, LING Xianyao, FENG Xinxin, ZHENG Haifeng, XU Yiwen. Short-term Traffic State Prediction Approach Based on FCM and Random Forest[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1879-1886. doi: 10.11999/JEIT171090

基于模糊C均值聚類(lèi)和隨機(jī)森林的短時(shí)交通狀態(tài)預(yù)測(cè)方法

doi: 10.11999/JEIT171090
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61601126, 61571129, U1405251),福建省基金(2016J01299)

Short-term Traffic State Prediction Approach Based on FCM and Random Forest

Funds: 

The National Natural Science Foundation of China (61601126, 61571129, U1405251), The Foundation of Fujian Province (2016J01299)

  • 摘要: 交通擁堵長(zhǎng)期以來(lái)是城市面臨的主要問(wèn)題之一,解決交通擁堵瓶頸刻不容緩。準(zhǔn)確的短時(shí)交通狀態(tài)預(yù)測(cè)有利于市民預(yù)知交通出行信息,及時(shí)采取措施避免陷入擁堵困境。該文提出一種基于模糊C均值聚類(lèi)(FCM)和隨機(jī)森林的短時(shí)交通狀態(tài)預(yù)測(cè)方法。首先,利用一種新穎的融合時(shí)空信息的自適應(yīng)多核支持向量機(jī)(AMSVM)來(lái)預(yù)測(cè)短時(shí)交通流參數(shù),包括流量、速度和占有率。其次,基于FCM算法分析歷史交通流,獲取歷史交通狀態(tài)信息。最后,利用隨機(jī)森林算法分析所預(yù)測(cè)的短時(shí)交通流參數(shù),得到最終預(yù)測(cè)的短時(shí)交通狀態(tài)。該方法在融合時(shí)空信息的同時(shí)采用隨機(jī)森林算法應(yīng)用于短時(shí)交通狀態(tài)預(yù)測(cè)這一全新的研究領(lǐng)域。實(shí)驗(yàn)結(jié)果表明,F(xiàn)CM對(duì)歷史交通狀態(tài)的評(píng)估方式適用于不同的高速路和城市道路場(chǎng)景。其次,隨機(jī)森林比其它常見(jiàn)的機(jī)器學(xué)習(xí)方法具有更高的預(yù)測(cè)精度,從而提供實(shí)時(shí)可靠的短時(shí)交通出行信息。
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出版歷程
  • 收稿日期:  2017-11-20
  • 修回日期:  2018-04-16
  • 刊出日期:  2018-08-19

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