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A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

Author

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  • Jiyun Lee

    (Department of Urban Planning, Hanyang University, Seoul 04763, Korea)

  • Donghyun Kim

    (Department of Urban Planning, Hanyang University, Seoul 04763, Korea)

  • Jina Park

    (Department of Urban Planning and Engineering, Hanyang University, Seoul 04763, Korea)

Abstract

Pedestrian-friendly cities are a recent global trend due to the various urbanization problems. Since humans are greatly influenced by sight while walking, this study identified the physical and visual characteristics of the street environment that affect pedestrian satisfaction. In this study, vast amounts of visual data were collected and analyzed using computer vision techniques. Furthermore, these data were analyzed through a machine learning prediction model and SHAP algorithm. As a result, every visual feature of the streetscape, for example, the visible area and urban design quality, had a greater effect on pedestrian satisfaction than any physical features. Therefore, to build a street with high pedestrian satisfaction, the perspective of pedestrians must be considered, and wide sidewalks, fewer lanes, and the proper arrangement of street furniture are required. In conclusion, visually, low enclosure, adequate complexity, and large green areas combine to create a highly satisfying pedestrian walkway. Through this study, we could suggest an approach from a visual perspective for the pedestrian environment of the street and see the possibility of using computer vision techniques.

Suggested Citation

  • Jiyun Lee & Donghyun Kim & Jina Park, 2022. "A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5730-:d:811612
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    References listed on IDEAS

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    2. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    3. Soongbong Lee & Myungjoo Han & Kyoungah Rhee & Bumjoon Bae, 2021. "Identification of Factors Affecting Pedestrian Satisfaction toward Land Use and Street Type," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    4. Zhaoya Gong & Qiwei Ma & Changcheng Kan & Qianyun Qi, 2019. "Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
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    Cited by:

    1. Sangwan Lee, 2022. "Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    2. Le Zhang & Xiaoxiao Xu & Yanlong Guo, 2022. "Comprehensive Evaluation of the Implementation Effect of Commercial Street Quality Improvement Based on AHP-Entropy Weight Method—Taking Hefei Shuanggang Old Street as an Example," Land, MDPI, vol. 11(11), pages 1-19, November.
    3. Natalia Distefano & Salvatore Leonardi & Nilda Georgina Liotta, 2023. "Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    4. Graziano Salvalai & Juan Diego Blanco Cadena & Gessica Sparvoli & Gabriele Bernardini & Enrico Quagliarini, 2022. "Pedestrian Single and Multi-Risk Assessment to SLODs in Urban Built Environment: A Mesoscale Approach," Sustainability, MDPI, vol. 14(18), pages 1-30, September.

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