Correlation between Land Use Pattern and Urban Rail Ridership Based on Bicycle-Sharing Trajectory

被引:1
作者
Li, Xiangyu [1 ]
Sinniah, Gobi Krishna [1 ]
Li, Ruiwei [1 ]
Li, Xiaoqing [1 ]
机构
[1] Univ Teknol Malaysia, Fac Built Environm & Surveying, Johor Baharu 81310, Malaysia
关键词
urban rail ridership; land use; bicycle-sharing; trip chain; random forest; BUILT ENVIRONMENT; TRAVEL BEHAVIOR; METRO; IMPACT;
D O I
10.3390/ijgi11120589
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As a form of rapid mass transportation, urban rail systems have always been widely used to alleviate urban traffic congestion and reconstruct urban structures. Land use characteristics are indispensable to this system and correlate with urban ridership. Dock-less bicycle-sharing expands the station service coverage range because it integrates public transportation with an urban rail system to create a convenient travel model. Consequently, the land use pattern with dock-less bicycle-sharing is associated with urban rail ridership. This paper measures the correlation between land use and urban rail ridership based on the trajectory of dock-less bicycle-sharing, which precisely reflects the travel behavior of passengers along the trip chain. The specific relationship has been determined using the random forest model. This paper found that the land use pattern could better explain the egress ridership during morning peak hours. In particular, it could explain 48.46% of the urban rail ridership in terms of egress, but the explicability for the ingress ridership slightly decreased to 36.88%. This suggests that the land use pattern is related to urban rail ridership. However, the impact situation varies, so we should understand this relationship with greater care.
引用
收藏
页数:23
相关论文
共 39 条
  • [1] Understanding the impact of built environment on metro ridership using open source in Shanghai
    An, Dadi
    Tong, Xin
    Liu, Kun
    Chan, Edwin H. W.
    [J]. CITIES, 2019, 93 : 177 - 187
  • [2] The Social Nestwork: Tree Structure Determines Nest Placement in Kenyan Weaverbird Colonies
    Angela Echeverry-Galvis, Maria
    Peterson, Jennifer K.
    Sulo-Caceres, Rajmonda
    [J]. PLOS ONE, 2014, 9 (02):
  • [3] The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling
    Ao, Yile
    Li, Hongqi
    Zhu, Liping
    Ali, Sikandar
    Yang, Zhongguo
    [J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2019, 174 : 776 - 789
  • [4] Are all transit stations equal and equitable? Calculating sustainability, livability, health, & equity performance of smart growth & transit-oriented-development (TOD)
    Appleyard, Bruce S.
    Frost, Alexander R.
    Allen, Christopher
    [J]. JOURNAL OF TRANSPORT & HEALTH, 2019, 14
  • [5] Examining influencing factors of bicycle usage for dock-based public bicycle sharing system: A case study of Xi'an, China
    Bai, Qiang
    Yu, Zhoulin
    Ma, Shuhong
    Wang, Yuanqing
    Agbelie, Bismark
    [J]. JOURNAL OF CLEANER PRODUCTION, 2022, 362
  • [6] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [7] Moving into and within cities - Interactions of residential change and the travel behavior and implications for integrated land use and transport planning strategies
    Bruns, Andre
    Matthes, Gesa
    [J]. TRAVEL BEHAVIOUR AND SOCIETY, 2019, 17 : 46 - 61
  • [8] Walking, cycling, and public transport for commuting and non-commuting travels across 5 European urban regions: Modal choice correlates and motivations
    Charreire, H.
    Roda, C.
    Feuillet, T.
    Piombini, A.
    Bardos, H.
    Rutter, H.
    Compernolle, S.
    Mackenbach, J. D.
    Lakerveld, J.
    Oppert, J. M.
    [J]. JOURNAL OF TRANSPORT GEOGRAPHY, 2021, 96
  • [9] Cheng L., 2022, Multimodal Transp, V1, DOI DOI 10.1016/J.MULTRA.2022.100004
  • [10] Examining non-linear built environment effects on elderly's walking: A random forest approach
    Cheng, Long
    De Vos, Jonas
    Zhao, Pengjun
    Yang, Min
    Witlox, Frank
    [J]. TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2020, 88