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基于数据驱动的城市快速路段拥堵状态辨识及诱导对象选择方法

赵坡 吴戈 王翔 汪思涵 昝雨尧

赵坡, 吴戈, 王翔, 汪思涵, 昝雨尧. 基于数据驱动的城市快速路段拥堵状态辨识及诱导对象选择方法[J]. 交通信息与安全, 2021, 39(6): 82-90. doi: 10.3963/j.jssn.1674-4861.2021.06.010
引用本文: 赵坡, 吴戈, 王翔, 汪思涵, 昝雨尧. 基于数据驱动的城市快速路段拥堵状态辨识及诱导对象选择方法[J]. 交通信息与安全, 2021, 39(6): 82-90. doi: 10.3963/j.jssn.1674-4861.2021.06.010
ZHAO Po, WU Ge, WANG Xiang, WANG Sihan, ZAN Yuyao. A Data-driven Method for Identifying Congestion State and Selecting Guided Vehicles for Urban Expressways[J]. Journal of Transport Information and Safety, 2021, 39(6): 82-90. doi: 10.3963/j.jssn.1674-4861.2021.06.010
Citation: ZHAO Po, WU Ge, WANG Xiang, WANG Sihan, ZAN Yuyao. A Data-driven Method for Identifying Congestion State and Selecting Guided Vehicles for Urban Expressways[J]. Journal of Transport Information and Safety, 2021, 39(6): 82-90. doi: 10.3963/j.jssn.1674-4861.2021.06.010

基于数据驱动的城市快速路段拥堵状态辨识及诱导对象选择方法

doi: 10.3963/j.jssn.1674-4861.2021.06.010
基金项目: 

国家自然科学基金项目 52002262

详细信息
    作者简介:

    赵坡(1995—), 硕士研究生.研究方向: 车辆出行特征与路网交通状态识别.E-mail: 981102018@qq.com

    通讯作者:

    王翔(1987—), 博士, 副教授.研究方向: 交通大数据分析与智能交通.E-mail: wangxiang@suda.edu.cn

  • 中图分类号: U491.4

A Data-driven Method for Identifying Congestion State and Selecting Guided Vehicles for Urban Expressways

  • 摘要: 交通诱导实施效果不佳的主要原因之一是具有差异性出行特征的出行者无法接受单一的诱导方案。针对城市快速路高峰时段拥堵问题, 研究了考虑车辆出行特征差异的交通诱导对象精准识别方法, 以保障诱导方案的实施效果。利用高德路况数据提取拥堵路段, 根据拥堵路段与相邻路段交通状态的相关性提出拥堵源路段识别方法; 利用车牌识别数据提取使用快速路车辆的出行特征, 包括快速路出行强度、地面道路出行强度、快速路出发时刻离散度和快速路路径选择多样性; 采用K-means++算法对车辆出行特征进行聚类, 识别出显著影响道路交通状态的出行者, 并为出行者推荐适合其出行特征的错峰或绕行诱导方案。以苏州快速路为例, 研究发现: 针对拥堵源路段的交通诱导能有效改善拥堵路段的交通状态; 类型3车辆(高频出行且易绕行)占单月工作日早高峰所有使用快速路车辆总数的14%, 却占单日早高峰总交通量的51%, 是重点诱导对象; 通过精准识别, 可推荐诱导车辆数占总车辆数的47%。

     

  • 图  1  K-means++计算流程

    Figure  1.  Flow of K-means++algorithm calculation

    图  2  快速路研究范围

    Figure  2.  Study scope of the expressways

    图  3  拥堵源路段诱导对象识别流程

    Figure  3.  Identification process of induction targets in congested-source sections

    图  4  拥堵路段开始与消散时刻分布

    Figure  4.  Time distribution of beginning and ending of congested sections

    图  5  拥堵源路段与拥堵路段时空热力图

    Figure  5.  Spatio-temporal thermal map of the congested source section and congested section

    图  6  车辆出行特征分布

    Figure  6.  Distribution of vehicle-travel characteristics

    图  7  聚类参数确定示意图

    Figure  7.  Determination of clustering parameters

    图  8  聚类结果分布

    Figure  8.  Distribution of clustering results

    图  9  拥堵源路段各类型出行者时变交通量分布

    Figure  9.  Time-varying traffic volume distribution of different travelers in congested-source road sections

    表  1  高德数据字段表

    Table  1.   Fields of the traffic-condition dataset of the Gaode map

    字段 中文名 示例
    roadid 路段编号 5********9
    roadname 路段名称 西环快速路
    roadclass 道路等级 快速路
    speed 速度/(km/h) 50
    stat_time 检测时刻 2020-03-16 T20:38:00
    下载: 导出CSV

    表  2  卡口数据字段表

    Table  2.   Fields of license plate recognition data

    字段 中文名 示例
    hphm 号牌号码 苏E*****
    dwbh 点位编号 3********7
    hpys 号牌颜色 蓝色
    jgsj 经过时刻 2020-03-16T12:30:07
    cdbh 车道编号 1
    sbbh 设备编号 3********1
    clsd 速度/(km/h) 50
    下载: 导出CSV

    表  3  各路段与拥堵源路段交通状况相关系数

    Table  3.   Correlation coefficient of traffic conditions between each road section and congested-source road section

    区域 路段编号 1 2 3 4 5 6 7 8
    a 6 0.75 0.78 0.81 0.84 0.92 1.00 0.36 -
    b 5 0.73 0.87 0.91 0.99 1.00 0.97 0.92 0.20
    c 7 0.57 0.76 0.80 0.92 0.92 0.96 1.00 0.51
    d 6 0.6 0.65 0.72 0.78 0.87 1.00 0.86 0.18
    下载: 导出CSV

    表  4  聚类结果统计

    Table  4.   Statistics of clustering results

    类型 车辆数(占比/%) 出行强度/d 快速路出行强度/d 地面道路出行强度/d 快速路出发时刻离散度 快速路路径多样性/个 快速路总出行次数(占比/%) 地面道路总出行次数(占比/%)
    1 155 229(17) 16.1 2.4 13.7 0.6 1.5 364 740(10) 2 132 801(54)
    2 147 435(16) 9.9 4.9 5.0 3.6 3.6 721 800(20) 734 499(19)
    3 121 396(14) 18.2 15.3 2.9 1.5 3.5 1 862 184(51) 346 732(9)
    4 474 278(53) 3.1 1.5 1.6 0.2 1.2 711 848(19) 736 385(19)
    下载: 导出CSV
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  • 收稿日期:  2021-03-06
  • 网络出版日期:  2022-01-12

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