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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于相似性聚类的交通流概率组合预测模型

王旭鹏 王梦灵

王旭鹏, 王梦灵. 基于相似性聚类的交通流概率组合预测模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210208001
引用本文: 王旭鹏, 王梦灵. 基于相似性聚类的交通流概率组合预测模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210208001
WANG Xupeng, WANG Mengling. Combination Forecast Model of Traffic Flow Probability Based on Similarity Clustering[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210208001
Citation: WANG Xupeng, WANG Mengling. Combination Forecast Model of Traffic Flow Probability Based on Similarity Clustering[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210208001

基于相似性聚类的交通流概率组合预测模型

doi: 10.14135/j.cnki.1006-3080.20210208001
基金项目: 国家自然科学基金(61673177);上海市“科技创新计划”人工智能专项(19DZ1209003);上海市经济和信息化委员会人工智能创新发展专项资金计划(2019-RGZN-01015)
详细信息
    作者简介:

    王旭鹏(1995—),男,浙江宁波人,硕士生,主要研究领域为交通流预测、交通大数据挖掘。E-mail:461520312@qq.com

    通讯作者:

    王梦灵,E-mail:wml_ling@ecust.edu.cn

  • 中图分类号: TP391.9

Combination Forecast Model of Traffic Flow Probability Based on Similarity Clustering

  • 摘要: 针对交通流呈现出周期性动态变化特征,充分挖掘并利用交通流数据潜在的时段相似性特征,提出了一种基于相似性聚类的交通流概率组合模型。首先采用自适应k-means++聚类方法对历史交通流数据进行聚类,将具有时段相似性的交通流数据进行分类,然后针对不同序列特征数据集构建组合模型。针对新输入的交通流状态数据,分析其与已分类数据的相似度计算组合模型的概率权重,然后通过将组合模型结果进行概率加权融合得到预测输出。仿真实验验证了本文模型的有效性与准确性。

     

  • 图  1  基于相似性聚类的交通流概率组合预测模型

    Figure  1.  Combination forecasting model of traffic flow probability based on similarity clustering

    图  2  自适应k-means++聚类流程

    Figure  2.  Adaptive k-means++ clustering process

    图  3  基于在线误差的自适应权重调节流程图

    Figure  3.  Flow chart of adaptive weight adjustment based on online error

    图  4  交通流量数据图

    Figure  4.  Traffic flow data graph

    图  5  不同k值的CHI

    Figure  5.  CHI with different k values

    图  6  PLS预测效果图

    Figure  6.  PLS prediction renderings

    图  7  预测模型效果对比图

    Figure  7.  Effect Comparison of prediction models

    表  1  预测指标对比

    Table  1.   Comparison of predictors

    ModelRMSEMAEMAPE/%
    PLS-LSTM30.9021.998.98
    PLS-LSTM(without clustering)32.8923.179.35
    ARIMA-BP32.9723.679.51
    LSTM33.5023.929.62
    SVR36.2429.1215.67
    下载: 导出CSV

    表  2  计算时间与精度关系

    Table  2.   Relationship between calculation time and accuracy

    Epocht/sRMSE
    2030.5631.96
    4042.2931.01
    6050.7530.90
    8061.0730.80
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-08
  • 网络出版日期:  2021-04-27

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