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

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  • 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
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    References listed on IDEAS

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    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.
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