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基于AFC数据的地铁车站留乘概率分布估计

陈欣 罗霞 朱颖 毛远思

陈欣, 罗霞, 朱颖, 毛远思. 基于AFC数据的地铁车站留乘概率分布估计[J]. 西南交通大学学报, 2022, 57(2): 418-424. doi: 10.3969/j.issn.0258-2724.20200270
引用本文: 陈欣, 罗霞, 朱颖, 毛远思. 基于AFC数据的地铁车站留乘概率分布估计[J]. 西南交通大学学报, 2022, 57(2): 418-424. doi: 10.3969/j.issn.0258-2724.20200270
CHEN Xin, LUO Xia, ZHU Ying, MAO Yuansi. Delayed-Boarding Probability Distribution for Metro Stations Using Auto Fare Collection Data[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 418-424. doi: 10.3969/j.issn.0258-2724.20200270
Citation: CHEN Xin, LUO Xia, ZHU Ying, MAO Yuansi. Delayed-Boarding Probability Distribution for Metro Stations Using Auto Fare Collection Data[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 418-424. doi: 10.3969/j.issn.0258-2724.20200270

基于AFC数据的地铁车站留乘概率分布估计

doi: 10.3969/j.issn.0258-2724.20200270
基金项目: 四川省科技厅科技计划(2020YJ0255)
详细信息
    作者简介:

    陈欣(1993—),男,博士研究生,研究方向为交通运输规划与管理,E-mail:swjtu_chenxin@163.com

    通讯作者:

    罗霞(1962—),女,教授,博士生导师,研究方向为交通运输规划与管理,E-mail:xia.luo@263.net

  • 中图分类号: U293.2

Delayed-Boarding Probability Distribution for Metro Stations Using Auto Fare Collection Data

  • 摘要:

    为研究地铁车站留乘特征,基于地铁自动售检票(auto fare collection, AFC)刷卡数据和运行图数据,研究了地铁车站留乘概率分布估计方法. 首先,基于乘客进、出站刷卡时刻与列车到、发时刻的关系,构造了聚集时间最大值、疏解时间的概率分布函数,提出了基于截断样本的聚集、疏解时间分布估计方法;其次,通过研究乘客进、出站刷卡时间、聚集时间、疏解时间及留乘次数间的关系,提出了地铁车站留乘概率分布估计方法;最后,以某地铁区段为例,在估计了留乘程度不同、类型不同车站的聚集、疏解时间分布的基础上,估计了这些车站在平峰、高峰时段内的留乘概率分布. 案例分析表明,在显著水平为5%的条件下,聚集、疏解时间分布估计结果可信;估计所得留乘概率分布与实地调查所得一致.

     

  • 图 1  地铁无换乘乘客可能搭乘列车示意

    Figure 1.  Feasible trains for passengers without transfer

    图 2  ${G_i}$含义示意

    Figure 2.  Schematic of the meaning of ${G_i}$

    图 3  区段示意

    Figure 3.  Schematic of sections

    图 4  在车站1、5搭乘各班次列车的乘客数量

    Figure 4.  Number of passengers boarding each train at stations 1 and 5

    表  1  乘客的可能行程特征列示

    Table  1.   Features of feasible passenger itineraries

    行程
    编号
    聚集时间留乘数/
    疏解
    时间
    搭乘列车
    编号
    1$ [0,{T_{ - ,1}} - {t_ + }) $0${t_ - } - {T_{ + ,1}}$1
    2$ [0,{T_{ - ,1}} - {t_ + }) $1${t_ - } - {T_{ + ,2}}$2
    3$ [0,{T_{ - ,1}} - {t_ + }) $2${t_ - } - {T_{ + ,3}}$3
    4$ [{T_{ - ,1}} - {t_ + },{T_{ - ,2}} - {t_ + }) $0${t_ - } - {T_{ + ,2}}$2
    5$ [{T_{ - ,1}} - {t_ + },{T_{ - ,2}} - {t_ + }) $1${t_ - } - {T_{ + ,3}}$3
    6$ [{T_{ - ,2}} - {t_ + },{T_{ - ,3}} - {t_ + }) $0${t_ - } - {T_{ + ,3}}$3
    下载: 导出CSV

    表  2  部分车站的聚集、疏解时间分布参数估计值

    Table  2.   Estimated distribution parameters for access and egress time at stations

    车站聚集时间疏解时间
    ${\mu _{{\text{A}} ,s,d} }$${\sigma _{{\text{A}} ,s,d} }$P ${\mu _{{\text{E}},s,d} }$${\sigma _{{\text{E}},s,d} }$P
    1 89.57 20.28 0.32
    2 99.45 40.45 0.15 101.26 39.73 0.67
    3 127.99 41.61 0.06 149.72 42.36 0.83
    4 10.23 8.73 0.53
    5 127.17 44.14 0.12
    6 57.40 26.43 0.98 63.82 27.86 0.99
    7 155.66 36.59 0.13 161.99 50.42 0.97
    8 110.04 26.73 0.36
    下载: 导出CSV

    表  3  不同时间段上行方向留乘概率分布参数估计与调查结果

    Table  3.   Estimated left-behind distribution parameters of delayed boarding and practical results in different periods

    车站时段$ {\beta _{s,d,q,0}} $$ {\beta _{s,d,q,1}} $$ {\beta _{s,d,q,2}} $$ {\beta _{s,d,q,3}} $$ {\beta _{s,d,q,4}} $$ {\beta _{s,d,q,5}} $${\beta _{s,d,q,6} }$
    估计调查估计调查估计调查估计调查估计调查估计调查估计调查
    1高峰 08:30—09:000.9741.0000.0030.022
    平峰 15:30—16:000.9961.0000.004
    2高峰 08:30—09:000.7120.7200.1640.1400.1050.1100.0130.0300.005
    平峰 15:30—16:000.9971.0000.0020
    3高峰 08:30—09:000.0020.0300.01400.0760.0600.2540.2800.5280.4900.1180.1200.0070.020
    平峰 15:30—16:000.9620.9800.0270.0200.0080.020
    5高峰 09:00—9:300.8420.8700.1250.1270.0070.0030.0140.0060.005
    平峰 20:00—20:300.9250.9310.0740.0690.0010
    6高峰 09:00—9:300.9850.9800.01300.0020.003
    平峰 20:00—20:300.9971.0000.0020.001
    7高峰 16:30—17:000.9100.9400.0550.0600.0240.0120
    平峰 09:00—09:300.9970.9900.0030.010
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-07
  • 修回日期:  2020-12-14
  • 刊出日期:  2020-12-25

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