Volume 41 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(2): 103-111
CHENG Cheng, CHEN Wendong, MA Hongsheng, LIU Xize, CHEN Xuewu. A Method for Identifying Operation Zones of Free-floating Shared Bikes Based on Leiden Algorithm: A Case Study of the City of Nanjing[J]. Journal of Transport Information and Safety, 2023, 41(2): 103-111. doi: 10.3963/j.jssn.1674-4861.2023.02.011
Citation: CHENG Cheng, CHEN Wendong, MA Hongsheng, LIU Xize, CHEN Xuewu. A Method for Identifying Operation Zones of Free-floating Shared Bikes Based on Leiden Algorithm: A Case Study of the City of Nanjing[J]. Journal of Transport Information and Safety, 2023, 41(2): 103-111. doi: 10.3963/j.jssn.1674-4861.2023.02.011
shu

A Method for Identifying Operation Zones of Free-floating Shared Bikes Based on Leiden Algorithm: A Case Study of the City of Nanjing

doi: 10.3963/j.jssn.1674-4861.2023.02.011
  • 1.

    Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China

  • 2.

    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China

  • 3.

    School of Transportation, Southeast University, Nanjing 211189, China

  • 4.

    Shenzhen General Integrated Transportation and Municipal Engineering Design & Research Institute Co., Ltd, Shenzhen 518003, Guangdong, China

  • Received Date: 2022-09-10
    Available Online: 2023-06-19
    Abstract
  • In the operation and management of the free-floating shared bike (FFSB) industry, the operation zones are mainly determined based on administrative boundaries of districts without fully considering the spatial distribu-tions of travel demand of FFSB, resulting in a large number of inter-zone transfer tasks which seriously deteriorates the efficiency of its operation. To this end, a new method for identifying operation zones of FFSB based on a Leiden community detection algorithm is developed using the bike order data from the City of Nanjing. A three-layer data structure of"travel OD (origin-destination)-traffic zone-spatial interaction network"is developed. The Leiden com-munity detection algorithm is used to identify the FFSB communities, which are taken as the operation sub-zones of FFBS to divide the operation zones. By comparing the communities of FFBS in different years, the temporal charac-teristics of the spatial distribution of FFBS travel are revealed. In addition, two indicators, network modularity and computational efficiency, are adopted to compare the performance of various community detection algorithms and to further verify the effectiveness and superiority of the Leiden algorithm in this research problem. The results show that: ①regarding the FFBS travel in 2019, the proposed algorithm identifies 23 activity communities, and the pro-portion of FFBS travel within the communities reaches 82.9%, which is higher than the traditional partition method by 11%. This indicates that the proposed algorithm can make more FFBS travel be classified within communities, in-crease the self-cycle rate of shared bikes within a zone, and improve the operational efficiency. ② Comparing to the case in 2019, the scale of communities decreased and the number of communities increases in 2022, implying a re-duction in the travel distance of FFBS users and a decrease in the proportion of inter-zone travel. ③ In terms of the results from the proposed algorithm, the network modularity reaches 0.55, which is significantly improved, compar-ing with the results of traditional CNM algorithm (0.2), Walktrap algorithm (0.31) and Louvain algorithm (0.42). The computation time of the proposed algorithm is 1.1 s, while for the other three algorithms, this value is 6.4, 1.6, and 1.4 s, respectively, which demonstrates the proposed algorithm has a significant improvement in computing effi-ciency. The above results show that the Leiden algorithm is superior to others in terms of partition quality and com-putational efficiency. The proposed method reveals that the spatial characteristics of FFBS travel and can obtain a better zonal management scheme for FFBS, which provides the theoretical guidance for the reasonable determina-tion of partition operation schemes of FFBS.

     

    • FullText(HTML)
  • loading
    • References(34)
  • [1]
    ZHU D, HUANG Z, SHI L, et al. Inferring spatial interaction patterns from sequential snapshots of spatial distributions[J]. International Journal of Geographical Information Science, 2018, 32(4): 783-805. doi: 10.1080/13658816.2017.1413192
    [2]
    吴雪颖. 地铁站域无桩共享单车骑行时空间特征及其影响因素研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.

    WU X Y. Exploring the spatio-temporal characteristics and in-fluencing factors of sharing bike integrated with metro sta-tions[D]. Harbin: Harbin Institute of Technology, 2019. (in Chinese)
    [3]
    程小丹. 基于GWR的共享单车出行特征及影响因素空间异质性研究[D]. 西安: 长安大学, 2019.

    CHENG X D. Spatial heterogeneity of influencing factors and characteristics of shared bicycle travel based on GWR[D]. Xi'an: Chang'an University, 2019. (in Chinese)
    [4]
    崔树强, 朱佩娟, 张美芳, 等. 城市建成环境对共享单车使用空间分布的影响: 以长沙市为例[J]. 西南大学学报(自然科学版), 2020, 42(6): 89-99. https://www.cnki.com.cn/Article/CJFDTOTAL-XNND202006011.htm

    CUI S Q, ZHU P J, ZHANG M F, et al. Influence of urban built environment on the spatial distribution of bike sharing use: The case of Changsha city[J]. Journal of Southwestern University(Natural Science Edition), 2020, 42(6): 89-99. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNND202006011.htm
    [5]
    袁朋伟, 董晓庆, 翟怀远, 等. 基于Nested Logit模型的共享单车选择行为研究[J]. 交通运输系统工程与信息, 2018, 18(5): 191-196. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201805028.htm

    YUAN P W, DONG X Q, ZHAI H Y, et al. Research on choice behavior of bike-sharing based on nested logit mod-el[J]. Journal of Transportation Systems Engineering and In-formation Technology, 2018, 18(5): 191-196. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201805028.htm
    [6]
    张昕明, 弓棣, 谢秉磊, 等. 计划行为理论视角下基于出行行为的公交防疫策略影响效果研究[J]. 交通信息与安全, 2021, 39(6): 117-125. doi: 10.3963/j.jssn.1674-4861.2021.06.014

    ZHANG X M, GONG D, XIE B L, et al. A study of the effec-tiveness of epidemic prevention policies on public transit us-age based on the theory of planned behaviors[J]. Journal of Transport Information and Safety, 2021, 39(6): 117-125. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.06.014
    [7]
    余周林. 共享单车影响下大学生出行行为分析及建模[D]. 西安: 长安大学, 2018.

    YU Z L. Analysis and modeling of college students'travel be-havior under the influence of shared bicycles[D]. Xi'an: Chang'an University, 2018. (in Chinese)
    [8]
    HUA M, CHEN X W, ZHENG S J, et al. Estimating the park-ing demand of free-floating bike sharing: A journey-da-ta-based study of Nanjing, China[J]. Journal of Cleaner Pro-duction, 2020, (244): 118764.
    [9]
    陈文栋. 城市轨道交通站点共享自行车停放设施配置研究——以南京市为例[D]. 南京: 东南大学, 2019.

    CHEN W D. Research on the configuration of bike-sharing parking facilities in urban rail transit stations: Taking Nanjing as an example[D]. Nanjing: Southeast University, 2019. (in Chinese)
    [10]
    张芳, 陈彬, 汤杨华, 等. 基于兴趣点聚类的无桩共享单车时空模式分析[J]. 系统仿真学报, 2019, 31(12): 2829-2836. doi: 10.16182/j.issn1004731x.joss.19-FZ0327

    ZHANG F, CHEN B, TANG Y H, et al. Spatio-temporal pat-tern analysis of free-floating bike sharing based on interest point clustering[J]. Journal of System Simulation, 2019, 31(12): 2829-2836. (in Chinese) doi: 10.16182/j.issn1004731x.joss.19-FZ0327
    [11]
    郭彦茹, 罗志雄, 王家川, 等. 数据驱动的共享单车停放区规划方法研究[J]. 交通运输系统工程与信息, 2021, 21(6): 9-16. doi: 10.16097/j.cnki.1009-6744.2021.06.002

    GUO Y R, LUO Z X, WANG J C, et al. Data-driven plan-ning and design for bike sharing parking spots[J]. Journal of Transportation Systems Engineering and Information Tech-nology, 2021, 21(6): 9-16. (in Chinese) doi: 10.16097/j.cnki.1009-6744.2021.06.002
    [12]
    李福, 徐良杰, 陈国俊, 等. 共享单车用户骑行起讫点时空特征分析[J]. 交通信息与安全, 2022, 40(3): 146-153, 170. doi: 10.3963/j.jssn.1674-4861.2022.03.015

    LI F, XU L J, CHEN G J, et al. An analysis of spatial-tempo-ral characteristics of origin and destination of shared-bike us-ers[J]. Journal of Transport Information and Safety 2022, 40(3): 146-153, 170. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.03.015
    [13]
    杨俊闯, 赵超. K-means聚类算法研究综述[J]. 计算机工程与应用, 2019, 55(23): 7-14, 63. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201923003.htm

    YANG J B, ZHAO C. Survey on k-means clustering algo-rithm[J]. Computer Engineering and Applications, 2019, 55(23): 7-14, 63.( in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201923003.htm
    [14]
    周强. 复杂网络社区发现算法研究[D]. 成都: 电子科技大学, 2020.

    ZHOU Q. Research on community discovery algorithms in complex networks[D]. Chengdu: University of Electronic Science and Technology, 2020. (in Chinese)
    [15]
    王欢. 基于网络社团结构的轨道交通线网生成研究[D]. 北京: 北京交通大学, 2019.

    WANG H. Rail transit network generation based on network community structure[D]. Beijing: Beijing Jiaotong Universi-ty, 2019. (in Chinese)
    [16]
    XU J, LI A, LI D, et al. Difference of urban development in China from the perspective of passenger transport around Spring Festival[J]. Applied Geography, 2017(87): 85-96.
    [17]
    余庆, 李玮峰, 杨东援. 基于手机信令数据的扬子江城市群空间联系结构分析[J]. 交通与运输, 2022, 38(3): 81-86. https://www.cnki.com.cn/Article/CJFDTOTAL-YSJT202203017.htm

    YU Q, LI W F, YANG D Y. Analysis of spatial structure in Yang-tze-River urban agglomeration using mobile phone data [J]. Traffic & Transportation, 2022, 38(3): 81-86. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSJT202203017.htm
    [18]
    ZHANG W, FANG C, ZHOU L, et al. Measuring megare-gional structure in the Pearl River Delta by mobile phone sig-naling data: A complex network approach[J]. Cities, 2020(104): 102809.
    [19]
    柯文前, 陈伟, 杨青. 基于高速公路流的区域城市网络空间组织模式: 以江苏省为例[J]. 地理研究, 2018, 37(9): 1832-1847. https://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ201809015.htm

    KE W Q, CHEN W, YANG Q. Regional urban network space organization mode based on expressway flow: Taking Jiangsu Province as an example[J]. Geographical Research, 2018, 37(9): 1832-1847. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ201809015.htm
    [20]
    徐进, 邓乐龄. 基于Louvain算法的铁路旅客社会网络社区划分研究[J]. 山东农业大学学报(自然科学版), 2018, 49(4): 722-725. https://www.cnki.com.cn/Article/CJFDTOTAL-SCHO201804035.htm

    XU J, DENG L L. Study on community detection of railway passenger social networks based on louvain algorithm[J]. Journal of Shandong Agricultural University (Natural Sci-ence Edition), 2018, 49(4): 722-725. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SCHO201804035.htm
    [21]
    蒋云, 杨文东. 改进Louvain算法的多层航线网络社区划分[J]. 北京交通大学学报, 2022, 46(2): 89-97. https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT202202011.htm

    JIANG Y, YANG W D. Community detection of multi-layer air transport network with improved louvain algorithm[J]. Journal of Beijing Jiaotong University, 2022, 46(2): 89-97. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT202202011.htm
    [22]
    YILDIRIMOGLU M, KIM J. Identification of communities in urban mobility networks using multi-layer graphs of net-work traffic[J]. Transportation Research Part C: Emerging Technologies, 2018(89): 254-267.
    [23]
    KIM K. Identifying the structure of cities by clustering using a new similarity measure based on smart card data[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 2002-2011.
    [24]
    ZHANG Y, MARSHALL S, CAO M, et al. Discovering the evolution of urban structure using smart card data: The case of London[J]. Cities, 2021 (112): 103157.
    [25]
    WU C, SMITH D, WANG M. Simulating the urban spatial structure with spatial interaction: A case study of urban polycentricity under different scenarios[J]. Computers, Environment and Urban Systems, 2021 (89): 101677.
    [26]
    DASTJERDI A M, MORENCY C. Bike-sharing demand prediction at community level under covid-19 using deep learning[J]. Sensors, 2022, 22(3): 1060
    [27]
    SONG J, ZHANG L, QIN Z, et al. A spatiotemporal dynamic analyses approach for dockless bike-share system[J]. Computers, Environment and Urban Systems, 2021 (85): 101566.
    [28]
    TRAAG V A, WALTMAN L, VAN ECK N J. From louvain to leiden: Guaranteeing well-connected communities[J]. Scientific Reports, 2019, 9 (1): 5233.
    [29]
    CHEN W D, CHEN X W, CHENG L, et al. Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network[J]. Journal of Transport Geography, 2022 (104): 103442.
    [30]
    中华人民共和国住房城乡建设部. 城市综合交通体系规划标准: GB/T 51328—2018[S]. 北京: 中国城市设计规划研究院, 2018.

    Ministry of Housing and Urban Rural Development of the People's Republic of China. Standard for urban comprehensive transportation system planning: GB/T 51328—2018[S]. Beijing: China Academy of Urban Design and Planning, 2018. (in Chinese)
    [31]
    中国城市规划设计研究院. 2021年中国主要城市共享单车/电单车骑行报告[EB/OL]. (2021-9)[2022-9-10]. https://www.thepaper.cn/newsDetail_forward_15381012 .

    China Academy of Urban Planning and Design. 2021 China principal cities sharing bikes and sharing electric bikes riding report[EB/OL]. (2021-9)[2022-9-10]. https://www.thepaper.cn/newsDetail_forward_15381012 .
    [32]
    CLAUSET A, NEWMAN M E J, MOORE C. Finding community structure in very large networks[J]. Physical Review E, 2004, 70 (6): 66111.
    [33]
    BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008 (10): P10008.
    [34]
    PONS P, LATAPY M. Computing communities in large networks using random walks[C]. International Symposium on Computer and Information Sciences, Istanbul, Turkey: Springer, 2005.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (827) PDF downloads(46) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return