IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i23p13256-d691583.html
   My bibliography  Save this article

Using POI Data to Identify the Demand for Pedestrian Crossing Facilities at Mid-Block

Author

Listed:
  • Weifeng Li

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

  • Jiawei He

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

  • Qing Yu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
    Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8568, Japan)

  • Yujiao Chang

    (College of Transportation Engineering, Chang’an University, Middle-Section of Nan’er Huan Road, Xi’an 710064, China)

  • Peng Liu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China)

Abstract

In Chinese cities, the widespread problem of the low density of the road network has seriously damaged the convenience of pedestrian crossing, resulting in an unfriendly pedestrian experience and restricted development of non-motorized traffic within the city. Only by accurately capturing the crossing needs of pedestrians can we adopt a targeted approach to improve the pedestrian crossing experience. In this paper, the demand and supply are considered synthetically, and a method of using point of interest (POI) data to analyze the demand for pedestrian crossing facilities at the mid-block is proposed. First, we developed the method of calculating the pedestrian crossing demand intensity based on POI data. Secondly, based on the appropriate length threshold and pedestrian crossing demand intensity threshold, a series of road sections with strong demand for pedestrian crossing facilities are identified in the study area. Finally, we use mobile phone data to obtain the intensity of residents’ activity in different areas, and find that the distribution of the areas with more activity is basically the same as that of the target road sections. The result shows that the method proposed in this paper can effectively identify the road sections with strong demand for crossing facilities at mid-block, and can provide support for the improvement of urban non-motorized traffic.

Suggested Citation

  • Weifeng Li & Jiawei He & Qing Yu & Yujiao Chang & Peng Liu, 2021. "Using POI Data to Identify the Demand for Pedestrian Crossing Facilities at Mid-Block," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13256-:d:691583
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/23/13256/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/23/13256/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moritz U. G. Kraemer & Adam Sadilek & Qian Zhang & Nahema A. Marchal & Gaurav Tuli & Emily L. Cohn & Yulin Hswen & T. Alex Perkins & David L. Smith & Robert C. Reiner & John S. Brownstein, 2020. "Mapping global variation in human mobility," Nature Human Behaviour, Nature, vol. 4(8), pages 800-810, August.
    2. Yang, Wenyue & Chen, Huiling & Wang, Wulin, 2020. "The path and time efficiency of residents' trips of different purposes with different travel modes: An empirical study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 88(C).
    3. Qing Yu & Weifeng Li & Haoran Zhang & Dongyuan Yang, 2020. "Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    4. Singleton, Patrick A. & Park, Keunhyun & Lee, Doo Hong, 2021. "Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data," Journal of Transport Geography, Elsevier, vol. 93(C).
    5. Eun-hye Yoo, 2019. "How Short Is Long Enough? Modeling Temporal Aspects of Human Mobility Behavior Using Mobile Phone Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(5), pages 1415-1432, September.
    6. Pfiester, Laura Mali & Thompson, Russell G. & Zhang, Lele, 2021. "Spatiotemporal exploration of Melbourne pedestrian demand," Journal of Transport Geography, Elsevier, vol. 95(C).
    7. Wenze Yue & Yang Chen & Qun Zhang & Yong Liu, 2019. "Spatial Explicit Assessment of Urban Vitality Using Multi-Source Data: A Case of Shanghai, China," Sustainability, MDPI, vol. 11(3), pages 1-20, January.
    8. Brightnes Risimati & Trynos Gumbo & James Chakwizira, 2021. "Spatial Integration of Non-Motorized Transport and Urban Public Transport Infrastructure: A Case of Johannesburg," Sustainability, MDPI, vol. 13(20), pages 1-17, October.
    9. Yunfeng Hu & Yueqi Han, 2019. "Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone," Sustainability, MDPI, vol. 11(5), pages 1-15, March.
    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. Guowei Luo & Jiayuan Ye & Jinfeng Wang & Yi Wei, 2023. "Urban Functional Zone Classification Based on POI Data and Machine Learning," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    2. Yao Chang & Dongbing Li & Zibibula Simayi & Shengtian Yang & Maliyamuguli Abulimiti & Yiwei Ren, 2022. "Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest," IJERPH, MDPI, vol. 19(13), pages 1-18, June.

    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. Bohong Zheng & Rui Guo & Komi Bernard Bedra & Yanfen Xiang, 2022. "Quantitative Evaluation of Urban Style at Street Level: A Case Study of Hengyang County, China," Land, MDPI, vol. 11(4), pages 1-28, March.
    2. Bo Liu & Desheng Xue & Yiming Tan, 2019. "Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China," Sustainability, MDPI, vol. 11(23), pages 1-26, December.
    3. Ziedan, Abubakr & Darling, Wesley & Brakewood, Candace & Erhardt, Greg & Watkins, Kari, 2021. "The impacts of shared e-scooters on bus ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 20-34.
    4. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    5. 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).
    6. Jinyao Lin & Yaye Zhuang & Yang Zhao & Hua Li & Xiaoyu He & Siyan Lu, 2022. "Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    7. Ting Liu & Gang Cheng & Jie Yang, 2023. "Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    8. Beibei Yu & Zhonghui Wang & Haowei Mu & Li Sun & Fengning Hu, 2019. "Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    9. Å pela VerovÅ¡ek & Tadeja ZupanÄ iÄ & Matevž JuvanÄ iÄ & Simon PetrovÄ iÄ & Matija Svetina & Miha Janež & Žiga PuÅ¡nik & Iztok Lebar Bajec & Miha MoÅ¡kon, 2021. "The Aspect of Mobility and Connectivity While Assessing the Neighbourhood Sustainability," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, May.
    10. Feuillet, Thierry & Bulteau, Julie & Dantan, Sophie, 2021. "Modelling context-specific relationships between neighbourhood socioeconomic disadvantage and private car use," Journal of Transport Geography, Elsevier, vol. 93(C).
    11. Anna Busłowska & Jacek Marcinkiewicz, 2023. "Social Cohesion of Functional Urban Areas (Example of Eastern Poland)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 451-473, June.
    12. Siyu Ma & Lin Yang & Mei-Po Kwan & Zejun Zuo & Haoyue Qian & Minghao Li, 2021. "Do Individuals’ Activity Structures Influence Their PM 2 . 5 Exposure Levels? Evidence from Human Trajectory Data in Wuhan City," IJERPH, MDPI, vol. 18(9), pages 1-27, April.
    13. Tongwen Wang & Ya Li & Haidong Li & Shuaijun Chen & Hongkai Li & Yunxing Zhang, 2022. "Research on the Vitality Evaluation of Parks and Squares in Medium-Sized Chinese Cities from the Perspective of Urban Functional Areas," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
    14. Yu, Ling & Zhao, Pengjun & Tang, Junqing & Pang, Liang, 2023. "Changes in tourist mobility after COVID-19 outbreaks," Annals of Tourism Research, Elsevier, vol. 98(C).
    15. Tianle Li & Xinqi Zheng & Chunxiao Zhang & Ruiguo Wang & Jiayu Liu, 2022. "Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China," Land, MDPI, vol. 11(6), pages 1-18, June.
    16. Jinghu Pan & Xiuwei Zhu & Xin Zhang, 2022. "Urban Vitality Measurement and Influence Mechanism Detection in China," IJERPH, MDPI, vol. 20(1), pages 1-24, December.
    17. Anqi Zhang & Weifeng Li & Jiayu Wu & Jian Lin & Jianqun Chu & Chang Xia, 2021. "How can the urban landscape affect urban vitality at the street block level? A case study of 15 metropolises in China," Environment and Planning B, , vol. 48(5), pages 1245-1262, June.
    18. Hailing Xu & Jianghong Zhu & Zhanqi Wang, 2019. "Exploring the Spatial Pattern of Urban Block Development Based on POI Analysis: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    19. Yani Lai & Zhen Lv & Chunmei Chen & Quan Liu, 2022. "Exploring Employment Spatial Structure Based on Mobile Phone Signaling Data: The Case of Shenzhen, China," Land, MDPI, vol. 11(7), pages 1-15, June.
    20. Jiangang Shi & Wei Miao & Hongyun Si & Ting Liu, 2021. "Urban Vitality Evaluation and Spatial Correlation Research: A Case Study from Shanghai, China," Land, MDPI, vol. 10(11), pages 1-15, November.

    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:13:y:2021:i:23:p:13256-:d:691583. 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.