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考虑乘客出行数据的城市轨道交通有效路径集生成方法
引用本文:殷世松, 卢缤程, 叶茂, 杨志强. 考虑乘客出行数据的城市轨道交通有效路径集生成方法[J]. 交通信息与安全, 2023, 41(2): 86-94. doi: 10.3963/j.jssn.1674-4861.2023.02.009
作者姓名:殷世松  卢缤程  叶茂  杨志强
作者单位:1.南京理工大学自动化学院 南京 210094;2.南京理工大学交通信息融合与系统控制工业和信息化部重点实验室 南京 210094;3.广州地铁集团有限公司 广州 510000
基金项目:国家重点研发计划项目(2017YFB1201202);
摘    要:有效路径集合生成是城市轨道交通断面客流预测、线网运力计算和客流分析的基础。为解决传统有效路径生成中存在的各路径有效程度无法评估、线性约束无法赋权的问题,降低问卷随机性对最终路径集生成的影响,本文在传统有效路径问卷调查数据的基础上,对乘客出行路径选择行为进行分析并做出假设,引入乘客出行时长,针对处于不同时长聚类簇下的有效路径分别建立评估模型,提出1种有效路径集生成方法。将轨道交通网络中站点和线路分别抽象为节点和边,构建轨道交通网络有向图;考虑出行路径类型、乘客出行主观因素以及乘客出行密度分布规律,利用自适应的DBSCAN算法处理乘客出行时长数据,以各时长下的出行密度为基准划分聚类簇,以聚类簇及其属性为输入,构建Logit模型并以其评估结果替代传统有效路径生成中的线性条件约束,并独立计算各簇所代表潜在有效路径的有效性权重,基于有效路径出行时长区间的连续性特点获取有效路径集。以广州地铁线网中多对出行OD为例进行验证,结果表明: 结合乘客出行数据聚类分析后所得到的有效路径集,调整兰德系数为0.652,相比于其他传统路径算法的生成结果,提升了0.379;同时在路径总时长-换乘次数平面上所产生的集合边界更为平滑,对复杂线网与快速变化的新开线网拥有更强的适应性。

关 键 词:城市轨道交通   有效路径   出行数据集   密度聚类   k短路算法
收稿时间:2022-06-23

A Method of Generating Effective Paths of Urban Rail Transit Based on Passenger Fare Collection Data
YIN Shisong, LU Bincheng, YE Mao, YANG Zhiqiang. A Method of Generating Effective Paths of Urban Rail Transit Based on Passenger Fare Collection Data[J]. Journal of Transport Information and Safety, 2023, 41(2): 86-94. doi: 10.3963/j.jssn.1674-4861.2023.02.009
Authors:YIN Shisong  LU Bincheng  YE Mao  YANG Zhiqiang
Affiliation:1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;2. MIIT Key Lab of Traffic Information Fusion & System Control, Nanjing University of Science and Technology, Nanjing 210094, China;3. Guangzhou Metro Group Co., Ltd, Guangzhou 510000, China
Abstract:It is a fundamental task to generate effective paths for predicting cross-section passenger flows, calculat-ing network capacity, and analyzing passenger demand of urban rail transit system. To solve the problems of tradi-tional effective path generation in which the validity of each path cannot be evaluated, and the linear constraint can-not be assigned, as well as aiming at reducing the influences of randomness of questionnaire on the final path set generation, a method of generating effective path sets is developed based on traditional survey data. The proposed method analyzes route choice behavior of passengers and makes corresponding hypothesis. Then, models for evalu-ating effective routes under different clusters are established by introducing passenger trip duration. Additionally, the stations and the lines between them are abstracted as nodes and edges of the network of rail transit. And then, considering route types, subjective factors, and the density of passenger flows, the passenger's travel time data are processed using the adaptive DBSCAN algorithm and are divided into clusters according to the density of passenger flows at different time intervals. Furthermore, taking the clustering results as the input, a Logit model is developed to replace the linear constraints in path generation. Weights of effective paths represented by clusters are calculated separately, and the effective path set is obtained based on its continuity characteristics at travel time intervals. Exper-iments are conducted based on multiple origin-destination trip data from the metro network in the City of Guang-zhou. Study results show that the effective paths have an average adjusted Rand index of 0.652. Compared with the traditional algorithms, the adjusted Rand index has improved by 0.379. It indicates that the proposed method produc-es a smoother set boundary in the plane of route length and transfer time, which is more adaptable to complex and changeable networks.
Keywords:Urban rail transit  Effective path  Travel data  Density-based clustering  K-short circuit algorithm
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