• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 58 Issue 3
Jun.  2023
Turn off MathJax
Article Contents
YANG Linchuan, ZHU Qing. Spatially Heterogeneous Effects of Built Environment on Travel Behavior of Older Adults[J]. Journal of Southwest Jiaotong University, 2023, 58(3): 696-703. doi: 10.3969/j.issn.0258-2724.20210689
Citation: YANG Linchuan, ZHU Qing. Spatially Heterogeneous Effects of Built Environment on Travel Behavior of Older Adults[J]. Journal of Southwest Jiaotong University, 2023, 58(3): 696-703. doi: 10.3969/j.issn.0258-2724.20210689

Spatially Heterogeneous Effects of Built Environment on Travel Behavior of Older Adults

doi: 10.3969/j.issn.0258-2724.20210689
  • Received Date: 02 Jul 2021
  • Rev Recd Date: 25 Jun 2022
  • Available Online: 07 Apr 2023
  • Publish Date: 12 Jul 2022
  • The implementation of the national strategy, actively addressing population aging, has underscored the importance of paying attention to the older adult population, which has become a crucial demographic group. Previous research has predominantly assumed that the effect of the built environment on the travel behavior of older adults is spatially fixed, failing to account for spatial heterogeneity. Therefore, to address this research gap, the propensity to travel, a travel behavior indicator, is analyzed using data from the 2011 Hong Kong Travel Characteristics Survey, and built-environment attributes are assessed using geo-data. A three-level random-intercept binary logistic regression model (level 1: individual, level 2: household, level 3: street block) and a geographically weighted binary logistic regression model are then developed to establish the complex relationship between the built environment and the propensity to travel of older adults, and the association is visualized with the help of ArcGIS platform. The findings demonstrate that population density, land use mix, intersection density, and streetscape greenery have a positive association with the propensity to travel of older adults, while accessibility to the metro and parks does not significantly affect this propensity. Moreover, the effects of all built-environment attributes on the propensity to travel vary across space. Specifically, the local effects of land-use mix on the propensity to travel are bi-directional: positive in the western part of the city and negative in the eastern part.

     

  • loading
  • [1]
    Department of Economic and Social Affairs. World population prospects 2019, volume Ⅱ: demographic profiles[M]. New York: United Nations, 2019.
    [2]
    李智轩,甄峰,张姗琪,等. 老年人公交移动性的季节时空分异特征研究:以安徽省芜湖市为例[J]. 地理科学进展,2021,40(2): 293-303. doi: 10.18306/dlkxjz.2021.02.010

    LI Zhixuan, ZHEN Feng, ZHANG Shanqi, et al. Seasonal and spatiotemporal differences in the public transport-based mobility of elderly population: a case study of Wuhu City in Anhui Province[J]. Progress in Geography, 2021, 40(2): 293-303. doi: 10.18306/dlkxjz.2021.02.010
    [3]
    CERVERO R, KOCKELMAN K. Travel demand and the 3Ds: density, diversity, and design[J]. Transportation Research Part D: Transport and Environment, 1997, 2(3): 199-219. doi: 10.1016/S1361-9209(97)00009-6
    [4]
    EWING R, CERVERO R. Travel and the built environment: a meta-analysis[J]. Journal of the American Planning Association, 2010, 76(3): 265-294. doi: 10.1080/01944361003766766
    [5]
    YANG L C, LIU J X, LU Y, et al. Global and local associations between urban greenery and travel propensity of older adults in Hong Kong[J]. Sustainable Cities and Society, 2020, 63: 102442.1-102442.12.
    [6]
    CHENG L, SHI K B, DE VOS J, et al. Examining the spatially heterogeneous effects of the built environment on walking among older adults[J]. Transport Policy, 2021, 100: 21-30. doi: 10.1016/j.tranpol.2020.10.004
    [7]
    LU Y, SARKAR C, XIAO Y. The effect of street-level greenery on walking behavior: evidence from Hong Kong[J]. Social Science & Medicine, 2018, 208: 41-49.
    [8]
    LU Y, YANG Y Y, SUN G B, et al. Associations between overhead-view and eye-level urban greenness and cycling behaviors[J]. Cities, 2019, 88: 10-18. doi: 10.1016/j.cities.2019.01.003
    [9]
    FENG J X. The influence of built environment on travel behavior of the elderly in urban China[J]. Transportation Research Part D: Transport and Environment, 2017, 52: 619-633. doi: 10.1016/j.trd.2016.11.003
    [10]
    YANG L C. Modeling the mobility choices of older people in a transit-oriented city: policy insights[J]. Habitat International, 2018, 76: 10-18. doi: 10.1016/j.habitatint.2018.05.007
    [11]
    SZETO W Y, YANG L C, WONG R C P, et al. Spatio-temporal travel characteristics of the elderly in an ageing society[J]. Travel Behaviour and Society, 2017, 9: 10-20. doi: 10.1016/j.tbs.2017.07.005
    [12]
    YANG H T, LUO P, Li C, et al. Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates[J]. Transportation Research Part A: Policy and Practice, 2023, 169: 103577.1-103577.16.
    [13]
    YANG L C, LIU J X, LIANG Y, et al. Spatially varying effects of street greenery on walking time of older adults[J]. ISPRS International Journal of Geo-Information, 2021, 10(9): 596.1-596.17.
    [14]
    XIAO Y, LU Y, GUO Y, et al. Estimating the willingness to pay for green space services in Shanghai: implications for social equity in urban China[J]. Urban Forestry & Urban Greening, 2017, 26: 95-103.
    [15]
    JAMES P, BANAY R F, HART J E, et al. A review of the health benefits of greenness[J]. Current Epidemiology Reports, 2015, 2(2): 131-142. doi: 10.1007/s40471-015-0043-7
    [16]
    LIU J X, WANG B, XIAO L Z. Non-linear associations between built environment and active travel for working and shopping: an extreme gradient boosting approach. Journal of Transport Geography, 2021, 92: 103034.1-103034.12.
    [17]
    YANG L C, AO Y B, KE J T, et al. To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults[J]. Journal of Transport Geography, 2021, 94: 103099.1-103099.10.
    [18]
    XIAO L Z, LO S, LIU J X, et al. Nonlinear and synergistic effects of TOD on urban vibrancy: applying local explanations for gradient boosting decision tree[J]. Sustainable Cities and Society, 2021, 72: 103063.1-103063.16.
    [19]
    SU L L, ZHOU S H, KWAN M P, et al. The impact of immediate urban environments on people’s momentary happiness[J]. Urban Studies, 2022, 59(1): 140-160. doi: 10.1177/0042098020986499
    [20]
    朱庆,陈兴旺,丁雨淋,等. 视觉感知驱动的三维城市场景数据组织与调度方法[J]. 西南交通大学学报,2017,52(5): 869-876. doi: 10.3969/j.issn.0258-2724.2017.05.005

    ZHU Qing, CHEN Xingwang, DING Yulin, et al. Organization and scheduling method of 3D urban scene data driven by visual perception[J]. Journal of Southwest Jiaotong University, 2017, 52(5): 869-876. doi: 10.3969/j.issn.0258-2724.2017.05.005
    [21]
    张昀昊,朱军,李维炼,等. 面向多样化终端的自适应网络三维可视化方法[J]. 西南交通大学学报,2019,54(5): 989-996. doi: 10.3969/j.issn.0258-2724.20180399

    ZHANG Yunhao, ZHU Jun, LI Weilian, et al. Adaptive web 3D visualization method for diverse terminals[J]. Journal of Southwest Jiaotong University, 2019, 54(5): 989-996. doi: 10.3969/j.issn.0258-2724.20180399
    [22]
    朱军,吴思豪,张昀昊,等. 大规模道路交通数据网络轻量化可视化方法[J]. 西南交通大学学报,2021,56(5): 905-912.

    ZHU Jun, WU Sihao, ZHANG Yunhao, et al. Lightweight web visualization of massive road traffic data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 905-912.
  • 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(2)  / Tables(3)

    Article views(405) PDF downloads(44) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return