基于自动售检票数据的城市轨道交通老年乘客出行特征研究

doi:10.16097/j.cnki.1009-6744.2026.05.023

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 272-282.DOI: 10.16097/j.cnki.1009-6744.2026.05.023

基于自动售检票数据的城市轨道交通老年乘客出行特征研究

刘晨辉^*a,b，陈升^a

湖南大学，a.土木工程学院；b.智慧道路与绿色交通湖南省普通高等学校重点实验室，长沙410082

收稿日期:2026-06-11 修回日期:2026-07-20 接受日期:2026-08-06 出版日期:2026-10-25 发布日期:2026-10-21
作者简介:刘晨辉（1987—），男，山东济宁人，教授，博士。
基金资助:
湖南省自然资源厅科技计划项目(HBZ20240122)。

Travel Patterns of Elderly Passengers in Urban Rail Transit Based on Automatic Fare Collection Data

LIU Chenhui^*a,b, CHEN Sheng^a

a. College of Civil Engineering; b. Key Laboratory of Smart Roads and Green Transport of Hunan Province, Hunan University, Changsha 410082, China

Received:2026-06-11 Revised:2026-07-20 Accepted:2026-08-06 Online:2026-10-25 Published:2026-10-21
Supported by:
Research Foundation of the Departmentof Natural Resources of Hunan Province (HBZ20240122)。

摘要/Abstract

摘要： 为精准有效地提升城市轨道交通的适老化服务水平，利用长沙地铁自动售检票数据分析城市轨道交通老年乘客的出行特征。首先，采用自然断点法按出行次数将老年乘客分为高、中、低频地铁出行，高频老年乘客人数占比仅6.13%，其地铁出行次数却占老年乘客出行次数的22.83%，周均地铁出行次数为14.28次，为老年乘客整体均值的3.72倍。接着，针对高频地铁出行老年乘客，基于出行天数基尼系数、小时熵、OD熵，周末工作日出行比，运用高斯混合模型进行聚类。结果表明：高频老年乘客可划分为3类：全周活跃型占老年乘客总客流59.88%，出行强度全周稳定、时空多样性高；工作日活跃型占老年乘客总客流30.74%，出行集中于工作日、周末骤降；两点一线型占比仅9.37%，出行时空高度固化，基尼系数0.072，OD熵1.10。与通勤人群相比，老年乘客出行距离更短、耗时更长，空间上向中心城区集聚，时间上晚高峰提前约1h、周末早高峰延后约1h。两阶段框架有效解析了老年乘客异质性，为地铁适老化分类施策提供了定量依据。

关键词: 城市交通, 出行特征, 聚类分析, 老年乘客, 城市轨道交通, 时空特征

Abstract: To improve the aging-friendly service level of urban rail transit precisely and effectively, this study analyzes the travel characteristics of elderly passengers using the Automatic Fare Collection (AFC) data from Changsha metro. First, the natural breaks method was applied to classify the elderly passengers into high-, medium-, and low-frequency metro user groups based on their number of trips. The high-frequency group accounted for only 6.13% of elderly passengers but contributed 22.83% of total elderly trips; their average weekly metro trips reached 14.28, which is 3.72 times of the overall average. Then, focusing on the high frequency elderly passengers, a Gaussian Mixture Model (GMM) was used for clustering based on the Gini coefficient of travel days, hourly entropy, OD entropy, and weekend-to-weekday travel ratio. The results show that the high-frequency elderly passengers can be divided into three types: (1) the full-week active type accounts for 59.88% of the total elderly passenger flow, with stable travel intensity throughout the week and high spatiotemporal diversity; (2) the weekday-active type, characterized by travel peaks on weekdays and a dramatic decline in trips during weekends, accounts for 30.74% of the total elderly passenger flow; and (3) the point-to-point type characterized by highly rigid spatiotemporal patterns, accounts for only 9.37%, with a Gini coefficient of 0.072 and an OD entropy of 1.10. Compared with commuters, the elderly passengers have shorter travel distances but longer travel times. Spatially, their trips are concentrated in the central urban area. Temporally, their evening peak is about one hour earlier, and the morning peak on weekends is about one hour later. This two-stage framework effectively reveals the heterogeneity of elderly passengers and provides a quantitative basis for formulating differentiated aging-friendly policies for metro systems.

Key words: urban transportation, travel patterns, cluster analysis, elderly passengers, urban rail transit, spatiotemporal characteristics

中图分类号:

U239.5

刘晨辉, 陈升. 基于自动售检票数据的城市轨道交通老年乘客出行特征研究[J]. 交通运输系统工程与信息, 2026, 26(5): 272-282.

LIU Chenhui, CHEN Sheng. Travel Patterns of Elderly Passengers in Urban Rail Transit Based on Automatic Fare Collection Data[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(5): 272-282.

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参考文献

[1]刘晨辉,苏美玲,王星宇,等.基于Beta随机效应模型的城市轨道交通客运分担率研究[J].湖南大学学报(自然科学版), 2025, 52(5): 154-161. [LIU C H, SU M L, WANG X Y, et al. Research on the passenger share rate of urban rail transit based on beta random effects model [J]. Journal of Hunan University (Natural Sciences), 2025, 52(5): 154-161.]

[2] 张政.老年人出行行为特征及其分析方法研究[D].北京: 北京交通大学, 2009. [ZHANG Z. Research on travel behavior characteristics of the elderly and its analysis methods[D]. Beijing: Beijing Jiaotong University, 2009.]

[3]黄建中,吴萌.特大城市老年人出行特征及相关因素分析: 以上海市中心城为例[J].城市规划学刊,2015 (2): 93-101. [HUANG J Z, WU M. Travel characteristics and related factors of the elderly in megacities: A case study of Shanghai Central City[J]. Urban Planning Forum, 2015(2): 93-101.]

[4]黄建中,马煜箫,胡刚钰.基于健康视角的老年人出行行为研究进展[J]. 科技导报, 2020, 38(7): 69-75. [HUANG J Z, MA Y X, HU G Y. Research progress on travel behavior of the elderly from the perspective of health[J]. Science & Technology Review, 2020, 38(7): 69-75.]

[5]FENG J, DIJST M, WISSINK B, et al. The impacts of household structure on the travel behaviour of seniors and young parents in China[J]. Journal of Transport Geography, 2013, 30: 117-126.

[6]卢顺达,徐正全,吴晓飞.2017年中国城市交通规划年会论文集.南昌市老年人出行特征分析[C].北京:中国建筑工业出版社, 2017. [LU S D, XU Z Q, WU X F. Proceedings of the 2017 China annual conference on urban transport planning. Analysis of travel characteristics of the elderly in Nanchang[C]. Beijing: China Architecture & Building Press, 2017.]

[7]翁剑成,张云飞,林鹏飞,等.基于就医活动推断的老年人公交就医可达性研究[J].交通运输系统工程与信息, 2024, 24(1): 221-229. [WENG J C, ZHANG Y F, LIN P F, et al. Study on bus-based healthcare accessibility for the elderly based on medical activity inference[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 221-229.]

[8] 吴静娴,钱依楠,韩印.考虑群体异质性的建成环境与老年人慢行出行关系研究[J].交通运输系统工程与信息, 2022, 22(4): 194-201. [WU J X, QIAN Y N, HAN Y. Study on the relationship between built environment and slow travel of the elderly considering group heterogeneity [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 194-201.]

[9] ZHOU Y, YUAN Q, YANG C. Transport for the elderly: Activity patterns, mode choices, and spatiotemporal constraints[J]. Sustainability (Switzerland), 2020, 12(23): 1-13.

[10] 邓洁榕,刘晨辉.基于GMM模型的城市轨道交通乘客通勤模式研究:以长沙市为例[J]. 都市快轨交通, 2025, 38(3): 48-53. [DENG J R, LIU C H. A study on commuting patterns of urban rail transit passengers based on Gaussian Mixture Model: A case study of Changsha[J]. Urban Rapid Rail Transit, 2025, 38(3): 48 53.]

[11] 刘晨辉, 谢瑶.基于地铁AFC数据的城际旅游客流出行特征研究:以长沙为例[J].湖南大学学报(自然科学版), 2026, 53(3): 119-126. [LIU C H, XIE Y. A study on the travel characteristics of intercity tourist passengers based on metro AFC data: A case study of Changsha[J]. Journal of Hunan University (Natural Sciences), 2026, 53 (3): 119-126.]

[12] KIM J, CORCORAN J, PAPAMANOLIS M. Route choice stickiness of public transport passengers: Measuring habitual bus ridership behaviour using smart card data [J]. Transportation Research Part C: Emerging Technologies, 2017, 83: 146-164.

[13] 长沙市统计局.长沙市第七次全国人口普查公报（第四号）[EB/OL]. (2021-06-03) [2026-04-05]. http://tjj. changsha.gov.cn/ tjxx/ tjsj/ tjgb/ sjgb/202106/t20210603_ 9988552.html. [Changsha Municipal Bureau of Statistics. The fourth bulletin of the seventh national population census of Changsha[EB/OL]. (2021-06-03)[2026-04 05]. http://tjj.changsha.gov.cn/tjxx/tjsj/tjgb/sjgb/202106/ t20210603_9988552.html.]

[14] 长沙市交通运输局. 2024年长沙市绿色出行宣传月和公交出行宣传周活动启动[EB/OL]. (2024 10-09)[2026-05-03]. http://jtysj.changsha.gov.cn/xxgk/ zwdt/ gzdt_43688/ 202410/t20241009_11615439.html. [Changsha Municipal Transport Bureau. Launch of the 2024 Changsha green travel promotion month and public transport travel promotion week[EB/OL]. (2024-10 09)[2026-05-03]. http://jtysj.changsha.gov.cn/xxgk/zwdt/ gzdt_43688/202410/t20241009_11615439.html.]

[15] 杨红, 申犁帆,胡议文,等.老年人地铁出行时空特征及与建成环境非线性关系:以武汉市为例[J].地理科学进展, 2023, 42(3): 491-504. [YANG H, SHEN L F, HU Y W, et al. Spatiotemporal characteristics of subway travel by older adults and their nonlinear relationship with the built environment: A case study of Wuhan[J]. Progress in Geography, 2023, 42(3): 491-504.]

基于自动售检票数据的城市轨道交通老年乘客出行特征研究

Travel Patterns of Elderly Passengers in Urban Rail Transit Based on Automatic Fare Collection Data

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