IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9328-d1167371.html
   My bibliography  Save this article

Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach

Author

Listed:
  • Duo Wang

    (College of Transportation Engineering, Chang’an University, Xi’an 710000, China)

  • Hong Chen

    (College of Transportation Engineering, Chang’an University, Xi’an 710000, China)

  • Chenguang Li

    (College of Transportation Engineering, Chang’an University, Xi’an 710000, China)

  • Enze Liu

    (College of Transportation Engineering, Chang’an University, Xi’an 710000, China)

Abstract

Traffic congestion is a critical problem in urban areas, and understanding the relationship between land use and congestion source is crucial for traffic management and urban planning. This study investigates the relationship between land-use characteristics and congestion pattern features of source parcels in the Second Ring Road of Xi’an, China. The study combines cell-phone data, POI data, and land-use data for the empirical analysis, and uses a spatial clustering approach to identify congested road sections and trace them back to source parcels. The correlations between building factors and congestion patterns are explored using the XGBoost algorithm. The results reveal that residential land and residential population density have the strongest impact on congestion clusters, followed by lands used for science and education and the density of the working population. The study also shows that a small number of specific parcels are responsible for the majority of network congestion. These findings have important implications for urban planners and transportation managers in developing targeted strategies to alleviate traffic congestion during peak periods.

Suggested Citation

  • Duo Wang & Hong Chen & Chenguang Li & Enze Liu, 2023. "Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9328-:d:1167371
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9328/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    2. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chenguang Li & Duo Wang & Hong Chen & Enze Liu, 2024. "Analysis of Urban Congestion Traceability: The Role of the Built Environment," Land, MDPI, vol. 13(2), pages 1-15, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Xiang & Chen, Xiaohong & Tian, Mingshu & De Vos, Jonas, 2023. "Effects of buffer size on associations between the built environment and metro ridership: A machine learning-based sensitive analysis," Journal of Transport Geography, Elsevier, vol. 113(C).
    2. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    3. Su, Shiliang & Zhao, Chong & Zhou, Hao & Li, Bozhao & Kang, Mengjun, 2022. "Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning," Journal of Transport Geography, Elsevier, vol. 100(C).
    4. Bo Wan & Xudan Zhao & Yuhan Sun & Tao Yang, 2023. "Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)," Sustainability, MDPI, vol. 15(10), pages 1-27, May.
    5. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    6. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    7. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    8. Houshmand Masoumi & Grzegorz Sierpiński, 2022. "The Land Use and Individual Correlates of Pedestrian Commuting: Who Walks to Their Work or Place of Study in the Large Cities of the MENA Region?," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    9. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    10. Su, Shiliang & Wang, Zhuolun & Li, Bozhao & Kang, Mengjun, 2022. "Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications," Journal of Transport Geography, Elsevier, vol. 104(C).
    11. Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).
    12. Zhenbao Wang & Jiarui Song & Yuchen Zhang & Shihao Li & Jianlin Jia & Chengcheng Song, 2022. "Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    13. Lixuan Zhao & Dewei Fang & Yang Cao & Shan Sun & Liu Han & Yang Xue & Qian Zheng, 2023. "Impact-Asymmetric Analysis of Bike-Sharing Residents’ Satisfaction: A Case Study of Harbin, China," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    14. Lixun Liu & Yujiang Wang & Robin Hickman, 2023. "How Rail Transit Makes a Difference in People’s Multimodal Travel Behaviours: An Analysis with the XGBoost Method," Land, MDPI, vol. 12(3), pages 1-23, March.
    15. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
    16. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    17. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).
    18. Jixiang Liu & Longzhu Xiao, 2024. "Socioeconomic differences in effect size: predicting commuting mode choice of migrants and locals using a light gradient boosting approach," Transportation, Springer, vol. 51(1), pages 1-24, February.
    19. Yousefzadeh Barri, Elnaz & Farber, Steven & Jahanshahi, Hadi & Beyazit, Eda, 2022. "Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms," Journal of Transport Geography, Elsevier, vol. 105(C).
    20. Lee, Yongsung & Lee, Bumsoo, 2022. "What’s eating public transit in the United States? Reasons for declining transit ridership in the 2010s," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 126-143.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9328-:d:1167371. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.