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基于双向长短期记忆网络的城市快速路合流区车速预测

谢济铭 夏玉兰 秦雅琴 赵荣达 刘兵 段国忠 陈金宏

谢济铭, 夏玉兰, 秦雅琴, 赵荣达, 刘兵, 段国忠, 陈金宏. 基于双向长短期记忆网络的城市快速路合流区车速预测[J]. 西南交通大学学报, 2024, 59(5): 1235-1244. doi: 10.3969/j.issn.0258-2724.20220005
引用本文: 谢济铭, 夏玉兰, 秦雅琴, 赵荣达, 刘兵, 段国忠, 陈金宏. 基于双向长短期记忆网络的城市快速路合流区车速预测[J]. 西南交通大学学报, 2024, 59(5): 1235-1244. doi: 10.3969/j.issn.0258-2724.20220005
XIE Jiming, XIA Yulan, QIN Yaqin, ZHAO Rongda, LIU Bing, DUAN Guozhong, CHEN Jinhong. Traffic Speed Prediction in Merging Zone of Urban Expressway Based on Bidirectional Long Short-Term Memory Network[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1235-1244. doi: 10.3969/j.issn.0258-2724.20220005
Citation: XIE Jiming, XIA Yulan, QIN Yaqin, ZHAO Rongda, LIU Bing, DUAN Guozhong, CHEN Jinhong. Traffic Speed Prediction in Merging Zone of Urban Expressway Based on Bidirectional Long Short-Term Memory Network[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1235-1244. doi: 10.3969/j.issn.0258-2724.20220005

基于双向长短期记忆网络的城市快速路合流区车速预测

doi: 10.3969/j.issn.0258-2724.20220005
基金项目: 国家重点研发计划(2018YFB1600500);国家自然科学基金项目(71861016)
详细信息
    作者简介:

    谢济铭(1994—),男,博士研究生,研究方向为智能车辆运动决策与路径规划,E-mail:xiejiming@stu.kust.edu.cn

    通讯作者:

    秦雅琴(1972—),女,教授,博士,研究方向为车路协同系统建模与优化,E-mail:qinyaqin@kust.edu.cn

  • 中图分类号: U491.1

Traffic Speed Prediction in Merging Zone of Urban Expressway Based on Bidirectional Long Short-Term Memory Network

  • 摘要:

    非典型复杂场景微观交通参数的准确预测是保证车路协同系统(IVICS)稳定运行的前提. 为解决IVICS条件下合流区高峰时段瓶颈现象所致的车速分布紊乱而不易预测的问题,首先,基于无人机高空视频,从广域视角提取交织区高峰时段全样本高精度车辆轨迹数据;然后,考虑双向长短期记忆网络(bidirectional long short-term memory,Bi-LSTM)时间较长且人工设置训练参数对模型预测性能影响较大,提出基于贝叶斯超参数(bayesian hyperparameters optimization,BHO)优化的BHO-Bi-LSTM 车速预测集成模型;最后,构建经典多元线性回归车速预测模型、Bi-LSTM车速预测模型作对比. 结果表明:BHO-Bi-LSTM模型表现最优,拟合优度、秩相关度分别为91.05%、94.87%,误差均值、误差的标准差、均方误差、均方根误差、归一化均方根误差分别为0.056 10.455 60.210 60.458 90.078 5,有效改善了合流区高峰时段车速特性复杂而导致不易预测的缺陷.

     

  • 图 1  模型框架

    Figure 1.  Model framework

    图 2  高空视频拍摄场景

    Figure 2.  High-altitude video shooting scene

    图 3  数据提取过程

    Figure 3.  Data extraction process

    图 4  微观轨迹信息提取结果

    Figure 4.  Results of microscopic trajectory information extraction

    图 5  平(高)峰时段交织区车速-位置分布曲线

    Figure 5.  Vehicle speed-position distribution curves in weaving area during flat (high) peak hours

    图 6  平(高)峰时段实例合流区车速分布

    Figure 6.  Distribution of traffic speeds in merging area during peak hours

    图 7  贝叶斯超参数优化过程

    Figure 7.  Bayesian hyperparameter optimization process

    图 8  交叉实验训练结果

    Figure 8.  Cross-experiment training results

    图 9  拟合对比

    Figure 9.  Fitting comparison

    图 10  误差直方图

    Figure 10.  Error bar graph

    图 11  逐样本误差点线图

    Figure 11.  Sample-wise errors

    图 12  秩相关对比

    Figure 12.  Rank correlation comparison

    表  1  模型总体评价指标对比

    Table  1.   Comparison of overall model evaluation indicators

    模型 R2 $\mu $ estD eMSE eRMSE eNRMSE rs
    多元线性回归[22]
    (模型Ⅰ)
    0.7980 0.001 7 1.659 5 2.754 2 1.659 6 0.269 0 0.844 0
    Bi-LSTM[23]
    (模型Ⅱ)
    0.8882 −0.005 3 0.471 1 0.221 8 0.470 9 0.080 6 0.945 3
    BHO-Bi-LSTM
    (模型Ⅲ)
    0.9105 0.056 1 0.455 6 0.210 6 0.458 9 0.078 5 0.948 7
    模型Ⅲ对比模型Ⅰ
    (提升或下降比例)
    +0.1125
    (↑14.10%)
    + 0.054 4
    (↓3 200%)
    −1.203 9
    (↑72.55%)
    −2.543 6
    (↑92.35%)
    −1.200 7
    (↑72.35%)
    −0.190 5
    (↑70.82%)

    +0.1047
    (↑12.35%)
    模型Ⅲ对比模型Ⅱ
    (提升或下降比例)
    0.2230
    (↑3.80%)
    + 0.061 4
    (↓1158.49%)
    −0.015 5
    (↑3.29%)
    −0.011 2
    (↑5.05%)
    −0.012 0
    (↑2.548%)
    −0.002 1
    (↑2.605%)
    +0.0034
    (↑0.40%)
    注:黑体加粗表示最优指标
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-03
  • 修回日期:  2022-05-25
  • 网络出版日期:  2024-07-13
  • 刊出日期:  2022-07-06

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