热带地理

热带地理 ›› 2021, Vol. 41 ›› Issue (5): 918-927.doi: 10.13284/j.cnki.rddl.003386

• 城市地理 • 上一篇    下一篇

广州地铁站点交通流特征与居民地铁出行的人群分异

叶昌东1(), 冯碧盈1, 姚华松2(), 代丹丹3   

  1. 1.华南农业大学 林学与风景园林学院，广州 510642
    2.广州大学，公共管理学院，广州 510006
    3.广州大学，管理学院，广州 510006
  • 收稿日期:2020-09-19 修回日期:2020-11-28 出版日期:2021-09-05 发布日期:2021-09-23
  • 通讯作者: 姚华松 E-mail:yechangdong@scau.edu.cn;yhscf@163.com
  • 作者简介:叶昌东（1983—），男，广东南雄人，教授，博士，研究方向为城市空间结构、城市公共空间、城市更新、乡村振兴等，（E-mail）yechangdong@scau.edu.cn；
  • 基金资助:
    国家自然科学基金(41871156);广东省自然科学基金项目(2017A030310668);大学生创新训练项目“空间正义观导向下的农民工市民化路径研究——以广州为例”(S202010564093)

Traffic Flow of Metro Stations and Population Travel Differentiation in Guangzhou

Changdong Ye1(), Biying Feng1, Huasong Yao2(), Dandan Dai3   

  1. 1.College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
    2.Public Administration School, Guangzhou University, Guangzhou 510006, China
    3.School of Management, Guangzhou University, Guangzhou 510006, China
  • Received:2020-09-19 Revised:2020-11-28 Online:2021-09-05 Published:2021-09-23
  • Contact: Huasong Yao E-mail:yechangdong@scau.edu.cn;yhscf@163.com

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摘要：

基于广州地铁交通流的始发地（Origin）—目的地（Destination）（OD分析），运用出行成本（距离/时间）、集中出行距离区间等指标对地铁站点交通流特征及居民地铁出行的人群分异进行刻画。结果表明：1）地铁站点随出行距离增加的乘客累积比例呈“S”型曲线变化，广州地铁站点的平均出行成本约为14.04 km（20.48 min），并由中心向外逐层增加约4 km（5 min）和13 km（10 min），周末平均出行成本略高于工作日。2）广州地铁出行的集中距离区间为8.55~26.61 km，在该出行距离范围内的乘客量占总数的71.88%；周末出行集中距离区间宽度变窄，但乘客量的集中比例却有所下降。3）社会弱势群体如女性、老年人、固定上下班的大学以上学历人群、办事人员、商服人员等是地铁潜在客流的主要构成群体；不同人群的地铁平均出行距离出现分化，其中出行需求小、出行能力偏弱的群体平均出行距离较短，出行需求大的群体平均出行距离普遍较长；除个别人群外站点集中出行距离区间相对差异不大。

关键词: 居民出行, 地铁交通, OD分析, 人群分异, 交通流, 广州

Abstract:

Metro railway is emerging as the optimal choice for residents' daily travel in many metropolitan areas in China, which profoundly affects the spatial-temporal characteristics of the travel mode of residents. Studying the spatio-temporal characteristics of metro traffic flow is of great practical significance for optimizing metro traffic layout and relieving urban traffic pressure. Reported studies have afforded significant achievements in the spatial-temporal characteristics of metro traffic flow; however, there are very few studies on the overall perspective of the station network, and most of them fail to further analyze population differences in using metro systems. In view of these gaps in the existing research, this study has two main objectives: 1) to describe the characteristics of metro traffic from the overall perspective of metro station network, with parameters of traffic flows within the station network, average travel distance (time), and their aggregation features; and 2) to analyze the differentiation of population groups near metro stations for understanding population differences in metro traffic flow. The Origin-Destination (OD) analysis method was used to calculate traffic flow characteristics between different metro stations, including the average travel cost (distance/time) and travel aggregation distance interval. Our results lead to the following inferences. 1) The cumulative proportion of passengers in metro stations along with travel distance show an "S" curve function feature, the average travel cost of metro stations in Guangzhou is approximately 14.04 km (20.48 min), and increase by ~4 km (~5 min) and ~13 km (~10 min) from the central area to the inner and outer suburbs. The average weekend travel costs are slightly higher than that of weekdays: the average travel cost on weekend ranged from 0.03 km (0.06 min) less in the central area to 0.32 km (0.49 min) and 0.64 km (0.77 min) more in the inner and outer suburbs, respectively. Three types of metro stations—those serving external transportation, business offices, and public units—have higher average travel costs. 2) The distance interval of passenger flow concentration in metro stations tends smoothly from the center to the periphery. The travel aggregation distance interval in the entire city is 8.55-26.61 km, with 71.88% passengers aggregated within this travel distance range. The travel aggregation distance interval in the central area and the inner and outer suburbs are 2.74-19.23, 7.49-25.23, and 24.3-46.73 km, respectively, with 78.87%, 71.81%, and 56.55% passengers, respectively. The spatial distributions on weekend and weekdays are almost the same, only with a narrow range of travel aggregation distance interval and smaller proportion of passengers on weekends than weekdays. The station types of external transportation, leisure tourism, and residential areas have higher travel aggregation distance interval and passenger proportion. 3) Population groups like women, elderly, people with college-level education or above, office staff, business people, and service personnel are the main components of metro passengers. The average metro travel distance of different population groups is differentiated, as groups with small travel demand and weak travel ability have short average travel distance, while groups with large travel demand generally have a long average travel distance. Our research contributes to the existing literature mainly in two aspects. 1) Average travel cost, knee point analysis, and travel aggregation distance interval were used to depict the spatial-temporal characteristics of metro traffic flow from the overall perspective of metro station network; this is a supplement and expansion of existing literature mainly based on stations or lines. 2) The differentiation of different population groups helped further optimize the planning, design, and line arrangement of metro systems.

Key words: residents' travel mode, metro traffic, OD analysis, population differentiation, traffic flow, Guangzhou

中图分类号: 

  • U293.6