Effect of built environment on shared bicycle reallocation: A case study on Nanjing, China

被引:61
|
作者
Zhao, De [1 ,2 ]
Ong, Ghim Ping [1 ]
Wang, Wei [2 ]
Hu, Xiao Jian [2 ]
机构
[1] Natl Univ Singapore, Dept Civil & Environm Engn, Block E1A,07-03,1 Engn Dr 2, Singapore 117576, Singapore
[2] Southeast Univ, Jiangsu Key Lab Urban ITS, Si Pai Lou 2, Nanjing 210096, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Built environment; Bicycle sharing; Bicycle demand; Reallocation; Zero-inflated negative binomial regression; SHARING SYSTEM; PUBLIC BIKES; IMPACT; PATTERNS; USAGE; WASHINGTON; PROGRAM; WEATHER; CHOICE; RUN;
D O I
10.1016/j.tra.2019.07.018
中图分类号
F [经济];
学科分类号
02 ;
摘要
Shared bicycles provide a convenient mobility option to commuters especially for short-distance trips. Nevertheless, it also presents a challenge to bicycle-sharing operators as they have to deal with reallocation issues, i.e. removing bicycles from parking facilities which are at or near capacity and refilling parking facilities that are in need of bicycles. Few studies in the literature have actually tried understanding why certain docking stations are prone to excessive demand or suffer from a lack of parking supply. This paper attempts to identify the demographic, built-environment and transport-infrastructure indicators that can potentially aid policy-makers and operators in identifying parking facilities prone to bicycle reallocation. In particular, we have adopted the bicycle sharing operations in Nanjing, China as a case study to understand how such indicators can be identified for appropriate parking infrastructural enhancements. To achieve this goal, this study has established zero-inflated negative binomial models using multi-source data including point-of-interest (POI), daily weather, transit stop location, demographic data and bike-share smart card data. The model results obtained from this study suggest that built environment correlates significantly to shared bicycle reallocation count. In general, bicycle docking stations with large reallocation counts are more likely to be found near residences, bus stops, metro stations, employment areas, restaurants, amenities, parks, sports facilities, and clinics/hospitals; while stations near entertainment facilities, places of attraction, hotels, shopping malls, and educational institution tend to have balanced demand and supply. Analysis on the elasticity values revealed that mean temperature and station capacity are the most influential factors in bicycle reallocation. Among all POIs, presence of restaurants and areas with high employment tend to exhibit strongly a need for morning bicycle removal and evening bicycle refilling at docked stations. Policy makers can provide actual guidelines in the planning of shared bicycle parking facilities using the findings and methodologies presented in this study.
引用
收藏
页码:73 / 88
页数:16
相关论文
共 50 条
  • [41] A Markovian model of user adaptation with case study of a shared bicycle scheme
    Zhang, Cen
    Schmocker, Jan-Dirk
    TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2019, 7 (01) : 223 - 236
  • [42] Relationship between Children's Independent Activities and the Built Environment of Outdoor Activity Space in Residential Neighborhoods: A Case Study of Nanjing
    Zhou, Yang
    Wang, Meng
    Lin, Siming
    Qian, Caiyun
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (16)
  • [43] Revealing the impact of built environment, air pollution and housing price on health inequality: an empirical analysis of Nanjing, China
    Ding, Yu
    Wang, Chenglong
    Wang, Jiaming
    Wang, Peng
    Huang, Lei
    FRONTIERS IN PUBLIC HEALTH, 2023, 11
  • [44] Spatio-temporal effects of built environment on running activity based on a random forest approach in nanjing, China
    Zhou, Wanyun
    Liang, Zhengyuan
    Fan, Zhengxi
    Li, Zhiming
    HEALTH & PLACE, 2024, 85
  • [45] Mechanism and Effect of Shantytown Reconstruction under Balanced and Full Development: A Case Study of Nanjing, China
    Yuan, Yaqi
    Song, Weixuan
    SUSTAINABILITY, 2020, 12 (19) : 1 - 16
  • [46] Dynamics in Mode Choice Decisions: A Case Study in Nanjing, China
    Ding, Ling
    Zhang, Ning
    GREEN INTELLIGENT TRANSPORTATION SYSTEM AND SAFETY, 2016, 138 : 31 - 40
  • [47] Analysis of Urban Built Environment Impacts on Outdoor Physical Activities-A Case Study in China
    Li, Bo
    Liu, Qiuhong
    Wang, Tong
    He, He
    Peng, You
    Feng, Tao
    FRONTIERS IN PUBLIC HEALTH, 2022, 10
  • [48] Numerical investigations on outdoor thermal comfort for built environment: case study of a Northwest campus in China
    Liang, Xiaoguang
    Tian, Wei
    Li, Richu
    Niu, Zhaoyang
    Yang, Xiaohu
    Meng, Xiangzhao
    Jin, Liwen
    Yan, Jinyue
    INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS, 2019, 158 : 6557 - 6563
  • [49] Investigating the Impacts of Urban Built Environment on Travel Energy Consumption: A Case Study of Ningbo, China
    Wu, Wei
    Xue, Binxia
    Song, Yan
    Gong, Xujie
    Ma, Tao
    LAND, 2023, 12 (01)
  • [50] Spatiotemporally heterogeneous willingness to ridesplitting and its relationship with the built environment: A case study in Chengdu, China
    Huang, Guan
    Qiao, Si
    Yeh, Anthony Gar-On
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 133