A Short-term Traffic Flow Combination Prediction model with Adaptive Weights
编号:117 访问权限:仅限参会人 更新:2021-12-03 10:14:18 浏览:129次 张贴报告

报告开始:2021年12月17日 08:56(Asia/Shanghai)

报告时间:1min

所在会场:[P1] Poster2020 [P1T1] Track 1 Advanced Transportation Information and Control Engineering

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摘要
A time-varying weighted combination model is proposed to predict the short-term traffic flow in the paper. The changed weights will much minimize the influence of random factors and generalize the application conditions. Firstly the training data is classified into several categories according to the traffic situations, and LS-SVM is used to train the single local prediction model of each category; Secondly the occurring possibility of different traffic situations is made as the weight of the corresponding local prediction model; and then the model is constructed with the linear weighted combination strategy. finally, the experiment with the actual speed data shows that the method is effective.
关键词
CICTP
报告人
xiaoliang sun
RIOH

稿件作者
xiaoliang sun RIOH
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重要日期
  • 会议日期

    12月17日

    2021

    12月20日

    2021

  • 12月16日 2021

    报告提交截止日期

  • 12月24日 2021

    注册截止日期

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Chinese Overseas Transportation Association
Chang'an University
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