Study on Topological and Statistical Characteristics of Shared-Bike Traffic Network

被引:0
|
作者
Sun, Heyang [1 ]
Cai, Xianhua [1 ]
Liu, Pei [1 ]
Jin, Kun [1 ]
机构
[1] Southeast Univ, Sch Transportat, Nanjing, Peoples R China
关键词
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Dockless shared bikes are playing an increasingly important role in slow traffic, and gradually become one of the main modes of "last kilometer". However, shared bikes often form unreasonable aggregation in operation for the spontaneous aggregation effect, which indicates that reassignment should be applied. Therefore, we construct a shared-bike traffic network, conduct a quantitative evaluation of the network characteristics, and provide reasonable bases for rebalancing of shared bikes to reduce unreasonable aggregation. In this paper, by using a novel cluster method based on spatial relation, a shared-bike traffic network of about 8000 nodes (clustering centers) is constructed and the it's topological and statistical characteristics are calculated. The result indicates that the growth of the shared-bike traffic network in Shanghai has reached a stable state and the network presents a significant scale-free feature. This study provides a scientific basis for the reassignment of shared bikes by bike suppliers, thus promoting the optimization and development of the shared-bike traffic network.
引用
收藏
页码:5155 / 5166
页数:12
相关论文
共 50 条
  • [1] A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model
    Qiao, Shaojie
    Han, Nan
    Huang, Jianbin
    Yue, Kun
    Mao, Rui
    Shu, Hongping
    He, Qiang
    Wu, Xindong
    [J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2021, 12 (06)
  • [2] Joint design of shared-bike and transit services in corridors
    Luo, Xiaoling
    Gu, Weihua
    Fan, Wenbo
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 132
  • [3] Research on shared-bike travel characterization and dynamic scheduling optimization
    Zhou, Liuci
    Wu, Wenxiang
    [J]. PROCEEDINGS OF 2024 INTERNATIONAL CONFERENCE ON COMPUTER AND MULTIMEDIA TECHNOLOGY, ICCMT 2024, 2024, : 583 - 590
  • [4] Shared-bike Demand Prediction Model Based on Station Clustering
    Qiao, Shao-Jie
    Han, Nan
    Yue, Kun
    Yi, Yu-Gen
    Huang, Fa-Liang
    Yuan, Chang-An
    Ding, Peng
    Gutierrez, Louis Alberto
    [J]. Ruan Jian Xue Bao/Journal of Software, 2022, 33 (04): : 1451 - 1476
  • [5] Dockless Shared-Bike Demand Prediction with Temporal Convolutional Networks
    Jin, Kun
    Wang, Wei
    Li, Shuang
    Liu, Pei
    Sun, Heyang
    [J]. CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 2851 - 2863
  • [6] Is digital finance environmentally friendly in China? Evidence from shared-bike trips
    Zhao, Chunkai
    Wang, Yuhang
    Ge, Zhenyu
    [J]. TRANSPORT POLICY, 2023, 138 : 129 - 143
  • [7] Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data
    Yang, Fan
    Ding, Fan
    Qu, Xu
    Ran, Bin
    [J]. SUSTAINABILITY, 2019, 11 (11)
  • [8] Exploring Shared-Bike Travel Patterns Using Big Data: Evidence in Chicago and Budapest
    Soltani, Ali
    Matrai, Tamas
    Camporeale, Rosalia
    Allan, Andrew
    [J]. COMPUTATIONAL URBAN PLANNING AND MANAGEMENT FOR SMART CITIES, 2019, : 53 - 68
  • [9] Using shared-bike orders to investigate the dynamicity of park service radii: Evidence from Shenzhen
    Zhou, Conghui
    Chen, Jiangyan
    Yang, Liuyi
    [J]. Journal of Environmental Management, 2024, 372
  • [10] Analysis of spatiotemporal mobility of shared-bike usage during COVID-19 pandemic in Beijing
    Chai, Xinwei
    Guo, Xian
    Xiao, Jihua
    Jiang, Jie
    [J]. TRANSACTIONS IN GIS, 2021, 25 (06) : 2866 - 2887