Passenger Flow Assignment Method for Urban Rail Transit Networks Based on Inference of Spatiotemporal Path
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摘要:
针对城市轨道交通客流分布推算问题，根据自动客票采集系统（AFC）数据和列车时刻表数据，提出基于乘客出行时空路径推算的网络客流分配方法. 首先，利用前述两类数据估算乘客出行时间参数；其次，使用基于插点法的可行路径搜索算法得到全网各OD （origin–destination）对的可行路径集合；再次，基于乘客进出站刷卡信息、列车时刻表数据及匹配的可行路径集合，构建乘客有效出行路径集和列车集的推算模型，获得有效出行结果集；进一步，结合所得有效结果集合与列车载客量限制，并根据列车时刻表完成列车运行推演，确定唯一的有效出行路径和所乘列车；最后，设计开发基于C# 语言的城市轨道交通网络客流推算系统，对某城市轨道交通工作日客流数据进行案例研究. 结果表明：客流推算系统所得的断面客流推算值与运营参照值的平均差异上、下行分别为2.03%、3.90%；列车满载率变化趋势符合线路路由特点；早晚高峰时段换乘站的换乘客流来源站点固定，但早高峰来源量比例较晚高峰稳定.
Abstract:To calculate passenger flow distribution in urban rail transits, a passenger flow assignment method based on inference of passenger spatiotemporal path is proposed with the data collected from the automatic fare collection (AFC) and train timetables. Firstly, the passenger travel time parameters are estimated with the above two types of data. The feasible path set of each origin–destination (OD) in the whole network is then obtained by using the feasible path search algorithm based on the node-inserting method. Subsequently, according to the inbound and outbound information from passenger smart cards, train timetable and matched feasible path set, an inference model of passenger effective travel path and train set is built to obtain the effective travel result set. Finally, a train operation is developed with the obtained effective result set, train load capacity, and train timetable to determine the sole effective travel path and riding train. A calculation system for the passenger flow in urban rail transit networks is designed and developed, and a case study is conducted on weekday passenger flow data of urban rail transit. The results show that the average difference of section passenger flow between the calculated results and operation reference data of upstream and downstream is 2.03% and 3.90%, respectively, and the trend of train load rate confirms to the line routing. Moreover, the source of transfer passenger flow at transfer station is stable in the morning and evening peaks, but the proportion of sources in the morning peak is more stable that in the evening peak.
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图 2单个乘客出行路段的可行列车集生成算法
Figure 2.Feasible train set algorithm for single passenger at path section
图 4基于插点法的可行路径搜索算法
Figure 4.Feasible path search algorithm based on the node-inserting method
图 6换乘路径可行列车集的前向判断算法
Figure 6.Forward judge algorithm for feasible train set of transfer path
图 8城市轨道交通客流推算系统架构
Figure 8.Calculation system framework for urban rail transit passenger flow
图 9YH线各区间全日断面客流对比
Figure 9.Comparison of daily section passenger flows in each section of YH line
图 10T3站各换乘方向的换乘量对比 （单位：人次）
Figure 10.Comparison of transfer volumes in each transfer direction at station T3 （unit: trips）
表 1乘客-路径-列车初始匹配结果类型
Table 1.Result types of matching initial passenger–route–train
类型 可行路径
情况可行列车
情况处理方式 类型 1 唯一 唯一 直接确定 类型 2 唯一 不唯一 算法 5 类型 3 不唯一 所有路径
均唯一算法 5 类型 4 不唯一 存在路径
不唯一算法 5 
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表 2输入的数据名称及内容
Table 2.Name and content of input data
数据名称 数据内容 乘客行程信息 进站时间、进站车站名、出站时间、出站车站名 时刻表信息 所属线路名、车次号、车站名、到站时刻、发车时刻 线路属性 线路名称、线路类型、是否开通、线路颜色（RGB） 车站属性 车站编号、车站名、是否为换乘站、衔接线路、衔接线路车站 满载率分级参数 分级数量、各线各区间分级标准 车站客流分级参数 线路名称、分级数量、全线分级标准、全线各站分级标准 下载:
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表 3YH线早晚高峰换乘客流来源统计
Table 3.Statistics on source of transfer passenger flow for YH line in morning and evening peaks
% 来源
站名早高峰来源
量比例来源
站名晚高峰来源
量比例RML 22.6 BSG 22.3 ZZHCZ 21.3 LCGC 21.6 LCGC 19.7 RML 20.1 BSG 19.6 ZZHCZ 19.7 YXY 16.9 QLL 16.3 标准差 1.91 标准差 2.08 下载:
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表 4EH线早晚高峰换乘客流来源统计
Table 4.Statistics on source of transfer passenger flow for EH line in morning and evening peaks
% 来源
站名早高峰来源
量比例来源
站名晚高峰来源
量比例NSH 22.0 LHDL 21.8 JW 20.8 JW 21.1 SWL 20.1 LZ 20.6 LZ 18.9 LL 18.4 HNC 18.2 HHYBG 18.0 标准差 1.35 标准差 1.51 下载:
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表 5列车区间满载率分级
Table 5.Classification of train-section load rate
% 等级 满载率范围 等级 满载率范围 等级 1 （0,50] 等级 4 （100,120] 等级 2 （50,80] 等级 5 （120,130] 等级 3 （80,100] 等级 6 （130,∞） 下载:
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