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基于机器学习的交通流预测方法综述

姚俊峰 何瑞 史童童 王萍 赵祥模

姚俊峰, 何瑞, 史童童, 王萍, 赵祥模. 基于机器学习的交通流预测方法综述[J]. 交通运输工程学报, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003
引用本文: 姚俊峰, 何瑞, 史童童, 王萍, 赵祥模. 基于机器学习的交通流预测方法综述[J]. 交通运输工程学报, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003
YAO Jun-feng, HE Rui, SHI Tong-tong, WANG Ping, ZHAO Xiang-mo. Review on machine learning-based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003
Citation: YAO Jun-feng, HE Rui, SHI Tong-tong, WANG Ping, ZHAO Xiang-mo. Review on machine learning-based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003

基于机器学习的交通流预测方法综述

doi: 10.19818/j.cnki.1671-1637.2023.03.003
基金项目: 

国家重点研发计划 2021YFC3001003

广东省科技计划项目 2017B030314076

详细信息
    作者简介:

    姚俊峰(1978-),男,山西晋城人,中国交通信息科技集团有限公司高级工程师,长安大学工学博士研究生,从事交通信息工程及控制研究

    赵祥模(1966-),男,重庆人,长安大学教授,工学博士

    通讯作者:

    王萍(1982-),女,山东泰安人,中山大学副教授,工学博士

  • 中图分类号: U495

Review on machine learning-based traffic flow prediction methods

Funds: 

National Key Research and Development Program of China 2021YFC3001003

Science and Technology Plan Project of Guangdong Province 2017B030314076

More Information
  • 摘要: 通过文献梳理、专家访谈和试验场景构建等方法,分析了道路指定断面和区域路网宏观交通流预测的国内外研究现状和发展趋势,归纳了局部断面交通流预测方法,包括传统机器学习、递归神经网络和混合模型,分析了卷积神经网络、图神经网络和融合多因素网络的特点,阐述了方法的原理、优势、局限性和应用场景,总结了现有场景交通数据集类别,从采样周期与采集方式角度归纳了国内外主流交通数据集。分析结果表明:递归神经网络可以有效获取交通数据的历史规律,但存在梯度爆炸、计算复杂度高、长时预测准确度不佳等问题;图神经网络针对路网拓扑连接关系引入了图结构,在考虑路网和交通流数据的时空相关性上具有明显优势;融合多因素网络充分考虑天气、道路、事故等内外部因素的影响,有效提升了交通流预测的实时性和鲁棒性;由于交通数据采集困难、外部因素影响难以量化、机器学习方法可解释性差等原因,交通流预测方法的改进受到了限制;未来应从交通信息有效挖掘和图卷积方法完善两方面入手,拓宽图结构在交通领域的应用和考虑非常态交通场景,进一步揭示交通数据的内在规律,开发更准确、高效的交通流预测方法,推动交通流预测在工业界的落地应用。

     

  • 图  1  陕西省西安市某指定断面交通流

    Figure  1.  Traffic flow at a designated section in Xi'an Shaanxi Province

    图  2  K-NN方法内部数据流传递关系

    Figure  2.  Transfer relationship of internal data flow in K-NN method

    图  3  RNN的折叠和展开结构

    Figure  3.  Folding and unfolding structures of RNN

    图  4  LSTM和GRU网络结构

    Figure  4.  Network structures of LSTM and GRU

    图  5  基于递归神经网络的交通流预测主流方法

    Figure  5.  Mainstream methods of traffic flow prediction based on recurrent neural network

    图  6  陕西省某区域交通流

    Figure  6.  Traffic flow in a certain area of Shaanxi Province

    图  7  交通场景下CNN的深度学习架构

    Figure  7.  Deep learning architecture of CNN in traffic scenarios

    图  8  交通数据到图像转换

    Figure  8.  Conversion of traffic data to image

    图  9  重新校准块

    Figure  9.  Rc block

    图  10  从CNN迁移到GCN

    Figure  10.  Migrating from CNN to GCN

    图  11  交通路网的图结构

    Figure  11.  Graph structures of traffic road network

    图  12  基于图卷积的交通流预测方法

    Figure  12.  Typical methods of traffic forecasting based on GCN

    图  13  十种典型方法的试验结果对比

    Figure  13.  Experimental result comparison of 10 typical methods

    图  14  STD方法框架

    Figure  14.  Framework of STD

    图  15  陕西省186个收费站点交通流数据

    Figure  15.  Traffic flow data of 186 toll stations in Shaanxi Province

    图  16  数据预处理流程

    Figure  16.  Preprocessing flowchart of data

    图  17  典型高峰异常数据

    Figure  17.  Abnormal data at typical peaks

    图  18  交通流数据的缺失模式

    Figure  18.  Missing patterns of traffic flow data

    表  1  三种断面交通流预测方法对比

    Table  1.   Comparison of three section traffic flow prediction methods

    方法 原理 局限性 适用场景
    传统机器学习 训练使用统计学知识和交通特征建立的回归方法 体系结构较浅,适用范围极其有限, 必须手动选取特征 预测时间短、精度需求低、数据量小的单一场景
    递归神经网络 借助记忆单元学习历史数据对当前的影响 长时依赖性获取不足,训练成本高 精度需求高与数据量大的单一场景
    混合模型 衔接几种方法或并行计算多个方法 未充分考虑路网的空间特性,计算复杂度高 预测精度和时间要求高的复杂场景
    下载: 导出CSV

    表  2  十种典型方法的试验结果对比

    Table  2.   Experimental result comparison of 10 typical methods

    方法 平均绝对误差 平均绝对离差 均方根误差 训练时间/min
    时序图卷积网络
    (Temporal Graph Convolutional Network,T-GCN)
    2.915/3.154
    3.429 /3.680
    0.709/0.711
    0.723/0.728
    8.532/9.019
    9.608 /10.129
    120
    STGCN 3.124/3.467
    3.826/4.174
    0.828/0.862
    0.831 /0.853
    9.199/10.371
    11.706/12.923
    22
    DCRNN 4.130/5.120
    6.430 /7.526
    0.964/1.120
    1.428/1.634
    9.650/10.620
    11.882 /12.949
    160
    时间卷积网络
    (Temporal Convolutional Network, TCN)
    5.372/9.305
    14.575 /18.953
    3.280/5.859
    10.040/13.153
    9.664/12.520
    19.574 /23.829
    64
    GRU 6.547/10.130
    16.570/21.105
    3.586/6.847
    11.544 /15.283
    9.981/15.328
    25.114/31.941
    78
    SAE 7.625/11.985
    16.980 /21.551
    3.626/5.897
    10.662/13.764
    10.136/15.525
    25.744 /32.743
    88
    LSTM 7.727/12.232
    17.246/21.921
    3.370/5.920
    10.626 /13.894
    10.332/16.354
    26.288/33.614
    96
    支持向量回归
    (Support Vector Regression,SVR)
    6.705/11.406
    22.808 /20.743
    3.923/6.478
    15.186/19.792
    20.017/34.670
    67.055 /87.619
    130
    ARIMA 12.094/22.913
    43.934/58.153
    7.605/14.742
    30.161 /40.058
    16.961/31.009
    57.026/75.063
    210
    历史平均
    (Historical Average, HA)
    14.602/26.620
    45.830 /60.245
    3.751/7.000
    12.583/16.61
    36.309/64.627
    108.666/142.22
    190
    下载: 导出CSV

    表  3  三种区域交通流预测方法对比

    Table  3.   Comparison of three regional traffic flow prediction methods

    方法 原理 优势 局限性 适用场景 后续应用
    卷积神经网络 采用2个连续的卷积层和池化层提取路网空间信息 局部连接性,输出神经元只连接到附近的局部输入神经元;池化机制,很大程度上减少了训练CNN所需的参数数量,同时保证了最重要的特征被保留;全连接性,应用于最后阶段的全连接层使得输入层的数据维度是可控的 在具有非欧几里得拓扑的运输网络中具有局限性 路网结构较为简单的场景 交通态势评估、通行能力分析与收费站人力规划
    图神经网络 利用图神经网络来建模交通网络中的空间依赖性,再利用卷积从根本上提高图分析效率 空间解释性好;图结构的引入有效地捕获了非欧结构的路网特性 未考虑天气等其他因素对路网的影响 路网结构复杂、外部因素影响弱的场景 交通信号协调控制与地铁车辆调度
    融合多因素 利用多源数据并将外部影响因素整合到方法中 实用性高;考虑外部因素对交通状况的影响 相关性因素的融合仍不够成熟 外部因素对交通状况影响较大的场景 交通事故分析
    下载: 导出CSV

    表  4  交通预测公开数据集

    Table  4.   Public datasets for traffic prediction

    分类 数据集名称 采样周期 采集方式
    固定式采集数据集 交通性能测量系统数据集 5 min 车辆检测器
    多尺度时空交通估计与预测数据集 5 min 车辆检测器
    洛杉矶高速公路数据集 5 min 环形检测器
    西雅图城市交通流数据集 5 min 地感线圈
    纽约市自行车共享行程数据集 自行车数据
    上海地铁客流数据集、深圳地铁客流数据集 15 min 地铁站点
    移动式采集数据集 马德里城市交通追踪数据集 0.5 s 车辆轨迹采集
    纽约市出租车和豪华轿车委员会行程数据集 出租车记录
    北京市出租车GPS轨迹数据集 30 min GPS数据
    上海市交通行程时间及车速数据集 10 min 出租车记录
    深圳市浮动车数据集 15 min 出租车轨迹
    北京市出租车GPS轨迹数据集 出租车轨迹
    滴滴出行交通流量预测数据集 网约车记录
    科隆市的推特交通信息语料库 1 s 车辆轨迹采集
    弗吉尼亚理工大学自然驾驶研究数据集 1 s 车辆轨迹采集
    下载: 导出CSV
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  • 收稿日期:  2022-12-15
  • 网络出版日期:  2023-07-07
  • 刊出日期:  2023-06-25

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