A space-time visualization analysis method for urban public bicycles connecting to the subway

doi:10.13474/j.cnki.11-2246.2020.0077

Abstract

Abstract: Public bicycle is an important connection method for urban rail transit. In order to show the time and space characteristics of public bicycle connection subway travel more clearly and intuitively, this paper proposes a time-space visual analysis method for public bicycle rental point to connect passenger traffic. The method firstly preprocesses and analyzes the IC card data of subway passenger flow and public bicycle, and analyzes the time series similarity between the two. Then, through the time cluster analysis of subway passenger flow, three types of travel day are obtained. Respectively, feature visualization and spatial information visualization for the correlation calculation results of each type of travel day, to achieve a comprehensive analysis of the time and space characteristics of public bicycle access subway travel. Finally, the method is used to Xuanwumen subway station in Xicheng District, Beijing, a time-space visual analysis of the public bicycle connection subway travel within a reasonable range of the station was carried out. The results show that the visual analysis of the integration of subway passenger flow and public bicycle IC card data can more comprehensively and dynamically grasp the spatial and temporal distribution of urban public bicycle connection subway travel. This method can be used for the planning of public bicycle rental points at urban rail transit stations and construction provides a quantitative basis.

Key words: public bicycle, connection, subway, correlation coefficient, visualization

CLC Number:

P208

GAO Zhiwen, ZHANG Xuedong, XU Zhijie, ZHANG Jianqin, SHEN Zhaomu, GUO Xiaogang, YANG Ming. A space-time visualization analysis method for urban public bicycles connecting to the subway[J]. Bulletin of Surveying and Mapping, 2020, 0(3): 48-55.

References

[1] 蒋源,陈小鸿,徐晓敏,等.公共自行车接驳轨道交通服务范围研究[J].交通运输系统工程与信息,2018,18(S1):94-102.
[2] 裴玉龙,潘跃.城市轨道交通站点接驳设施规模预测方法[J].交通信息与安全,2018,36(4):106-112.
[3] 周强,吴戈,孙瀚.作为地铁接驳手段的公共自行车使用特性分析[J].交通运输系统工程与信息,2015,15(3):179-184.
[4] 沈翔. 基于城市轨道交通换乘的公共自行车租赁点选址规划研究[D].西安:长安大学,2015.
[5] YANG M, LIU X, WANG W, et al. Empirical analysis of a mode shift to using public bicycles to access the suburban metro: survey of Nanjing,China[J].Journal of Urban Planning&Development,2016,142(2):237-239.
[6] VOGEL P, GREISER T, MATTFELD D C. Understanding bike-sharing systems using data mining: exploring activity patterns[J]. Procedia - Social and Behavioral Sciences, 2011, 20(1):514-523.
[7] 许丹丹. 北京市公共自行车系统优化关键技术研究[D].北京:北京工业大学,2017.
[8] 叶益芳.城市轨道交通车站不同接驳方式合理吸引范围研究[J].铁道运输与经济,2014,36(6):77-81.
[9] LAI J Z C, HUANG T J. Fast global k-means clustering using cluster membership and inequality[J]. Pattern Recognition, 2010, 43(5):1954-1963.
[10] 牛丹丹,段宗涛,陈柘,等.城市出租车乘客出行特征可视化分析方法[J].计算机工程与应用,2019,55(6):237-243.
[11] ZHANG J Q, QIU P Y, DUAN Y C, et al. A space-time visualization analysis method for taxi operation in Beijing[J]. Journal of Visual Languages & Computing, 2015, 31(31):1-8.
[12] GEBHART K, NOLAND R B. The impact of weather conditions on bikeshare trips in Washington, DC[J]. Transportation, 2014, 41(6):1205-1225.
[13] LOAIZA-MONSALVE D, RIASCOS A P. Human mobility in bike-sharing systems: structure of local and non-local dynamics[J].PloS ONE,2019,14(3). DOI: 10.1371/journal.pone.0213106.
[14] DU Y, DENG F, LIAO F. A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system[J]. Transportation Research Part C: Emerging Technologies, 2019, 103:39-55.
[15] AI Y, LI Z P, GAN M. A solution to measure traveler’s transfer tolerance for walking mode and dockless bike-sharing mode[J]. Journal of Supercomputing, 2018, 75(1):1-18.