IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i8p1308-d1458579.html
   My bibliography  Save this article

Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning

Author

Listed:
  • Kuntong Huang

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Taiyang Wang

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Xueshun Li

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Ruinan Zhang

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Yu Dong

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

Abstract

Restoring campus pedestrian spaces is vital for enhancing college students’ mental well-being. This study objectively and thoroughly proposed a reference for the optimization of restorative campus pedestrian spaces that are conducive to the mental health of students. Eye-tracking technology was employed to examine gaze behaviors in these landscapes, while a Semantic Difference questionnaire identified key environmental factors influencing the restorative state. Additionally, this study validated the use of virtual reality (VR) technology for this research domain. Building height difference (HDB), tree height (HT), shrub area (AS), ground hue (HG), and ground texture (TG) correlated significantly with the restorative state (ΔS). VR simulations with various environmental parameters were utilized to elucidate the impact of these five factors on ΔS. Subsequently, machine learning models were developed and assessed using a genetic algorithm to refine the optimal restorative design range of campus pedestrian spaces. The results of this study are intended to help improve students’ attentional recovery and to provide methods and references for students to create more restorative campus environments designed to improve their mental health and academic performance.

Suggested Citation

  • Kuntong Huang & Taiyang Wang & Xueshun Li & Ruinan Zhang & Yu Dong, 2024. "Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning," Land, MDPI, vol. 13(8), pages 1-26, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:8:p:1308-:d:1458579
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/8/1308/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/8/1308/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahsa Farahani & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Soo-Mi Choi, 2023. "A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes," Sustainability, MDPI, vol. 15(13), pages 1-30, July.
    2. Catherine Sundling & Marianne Jakobsson, 2023. "How Do Urban Walking Environments Impact Pedestrians’ Experience and Psychological Health? A Systematic Review," Sustainability, MDPI, vol. 15(14), pages 1-32, July.
    3. Ming Lu & Jingwan Fu, 2019. "Attention Restoration Space on a University Campus: Exploring Restorative Campus Design Based on Environmental Preferences of Students," IJERPH, MDPI, vol. 16(14), pages 1-19, July.
    4. Svein Åge Kjøs Johnsen & Marin Kristine Brown & Leif Werner Rydstedt, 2022. "Restorative experiences across seasons? Effects of outdoor walking and relaxation exercise during lunch breaks in summer and winter," Landscape Research, Taylor & Francis Journals, vol. 47(5), pages 664-678, July.
    5. Qiaohui Liu & Xiaoping Wang & Jinglan Liu & Guolin Zhang & Congying An & Yuqi Liu & Xiaoli Fan & Yishen Hu & Heng Zhang, 2021. "The Relationship between the Restorative Perception of the Environment and the Physiological and Psychological Effects of Different Types of Forests on University Students," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
    Full references (including those not matched with items on IDEAS)

    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. Yanlong Guo & Ke Wang & Han Zhang & Zuoqing Jiang, 2022. "Soundscape Perception Preference in an Urban Forest Park: Evidence from Moon Island Forest Park in Lu’an City," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    2. Wanting Liu & Ning Sun & Jingyu Guo & Zhenhua Zheng, 2022. "Campus Green Spaces, Academic Achievement and Mental Health of College Students," IJERPH, MDPI, vol. 19(14), pages 1-10, July.
    3. Liu, Wan-Yu & Tsao, Chen & Lin, Chun-Cheng, 2023. "Tourists' preference for colors of forest landscapes and its implications for forest landscape planning policies," Forest Policy and Economics, Elsevier, vol. 147(C).
    4. Song Song & Ruoxiang Tu & Yao Lu & Shi Yin & Hankun Lin & Yiqiang Xiao, 2022. "Restorative Effects from Green Exposure: A Systematic Review and Meta-Analysis of Randomized Control Trials," IJERPH, MDPI, vol. 19(21), pages 1-22, November.
    5. Eduard Alexandru Dumitru & Rozi Liliana Berevoianu & Valentina Constanta Tudor & Florina-Ruxandra Teodorescu & Dalila Stoica & Andreea Giucă & Diana Ilie & Cristina Maria Sterie, 2023. "Climate Change Impacts on Vegetable Crops: A Systematic Review," Agriculture, MDPI, vol. 13(10), pages 1-18, September.
    6. Xialu Wu & Yu-Sheng Shen & Shenghui Cui, 2023. "Global Trends in Green Space and Senior Mental Health Studies: Bibliometric Review," IJERPH, MDPI, vol. 20(2), pages 1-12, January.
    7. Chongxian Chen & Weijing Luo & Ning Kang & Haiwei Li & Xiaohao Yang & Yu Xia, 2020. "Serial Mediation of Environmental Preference and Place Attachment in the Relationship between Perceived Street Walkability and Mood of the Elderly," IJERPH, MDPI, vol. 17(13), pages 1-18, June.
    8. Huojin Chen & Jian-Hong Ye, 2023. "The Influence of Outdoor Activities and Campus Landscape on University Students’ Subjective Well-Being during the COVID-19 Pandemic," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    9. Das, Priyanka & Maitra, Swati, 2024. "Priority areas of intervention for improving pedestrian infrastructure and facilities at tourist destinations in India," Transport Policy, Elsevier, vol. 145(C), pages 126-136.
    10. Argyro Anna Kanelli & Maria Lydia Vardaka & Chrisovaladis Malesios & Zainab Jamidu Katima & Olga-Ioanna Kalantzi, 2024. "Can Campus Green Spaces Be Restorative? A Case Study from Tanzania," Sustainability, MDPI, vol. 16(3), pages 1-21, January.
    11. Hanbin Shen & Xuecong He & Jing He & Danming Li & Mingjie Liang & Xubin Xie, 2024. "Back to the Village: Assessing the Effects of Naturalness, Landscape Types, and Landscape Elements on the Restorative Potential of Rural Landscapes," Land, MDPI, vol. 13(7), pages 1-30, June.
    12. Jun Zhang & Jinghua Jin & Yimeng Liang, 2024. "The Impact of Green Space on University Students’ Mental Health: The Mediating Roles of Solitude Competence and Perceptual Restoration," Sustainability, MDPI, vol. 16(2), pages 1-28, January.
    13. Bixue Huang & Yang Zhao & Jiahao Yang & Wanying Wang & Tongye Guo & Xinyi Luo & Meng Du, 2024. "Thermal Comfort and Restorative Benefits of Waterfront Green Spaces for College Students in Hot and Humid Regions," Sustainability, MDPI, vol. 16(20), pages 1-24, October.
    14. Yiwen Liu & Junjie Zhang & Chunlu Liu & Yang Yang, 2024. "A Review of Attention Restoration Theory: Implications for Designing Restorative Environments," Sustainability, MDPI, vol. 16(9), pages 1-19, April.
    15. Su Wang & Huaidong He & Fulong Li & Qingqing Xiao, 2023. "A Study on the Soundscape of Underground Commercial Space in Lu’an City and Hefei City, China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    16. Tianning Yao & Yao Xu & Liang Sun & Pan Liao & Jin Wang, 2024. "Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street," Land, MDPI, vol. 13(9), pages 1-27, August.

    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:jlands:v:13:y:2024:i:8:p:1308-:d:1458579. 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.