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基于模糊C均值聚类和随机森林的短时交通状态预测方法

陈忠辉 凌献尧 冯心欣 郑海峰 徐艺文

陈忠辉, 凌献尧, 冯心欣, 郑海峰, 徐艺文. 基于模糊C均值聚类和随机森林的短时交通状态预测方法[J]. 电子与信息学报, 2018, 40(8): 1879-1886. doi: 10.11999/JEIT171090
引用本文: 陈忠辉, 凌献尧, 冯心欣, 郑海峰, 徐艺文. 基于模糊C均值聚类和随机森林的短时交通状态预测方法[J]. 电子与信息学报, 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均值聚类和随机森林的短时交通状态预测方法

doi: 10.11999/JEIT171090
基金项目: 

国家自然科学基金(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: 交通拥堵长期以来是城市面临的主要问题之一,解决交通拥堵瓶颈刻不容缓。准确的短时交通状态预测有利于市民预知交通出行信息,及时采取措施避免陷入拥堵困境。该文提出一种基于模糊C均值聚类(FCM)和随机森林的短时交通状态预测方法。首先,利用一种新颖的融合时空信息的自适应多核支持向量机(AMSVM)来预测短时交通流参数,包括流量、速度和占有率。其次,基于FCM算法分析历史交通流,获取历史交通状态信息。最后,利用随机森林算法分析所预测的短时交通流参数,得到最终预测的短时交通状态。该方法在融合时空信息的同时采用随机森林算法应用于短时交通状态预测这一全新的研究领域。实验结果表明,FCM对历史交通状态的评估方式适用于不同的高速路和城市道路场景。其次,随机森林比其它常见的机器学习方法具有更高的预测精度,从而提供实时可靠的短时交通出行信息。
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出版历程
  • 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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