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Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches

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  • Pawinee Iamtrakul

    (Center of Excellence in Urban Mobility Research and Innovation, Faculty of Architecture and Planning, Thammasat University, Pathumthani 12120, Thailand)

  • Sararad Chayphong

    (Center of Excellence in Urban Mobility Research and Innovation, Faculty of Architecture and Planning, Thammasat University, Pathumthani 12120, Thailand)

  • Pittipol Kantavat

    (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

  • Yoshitsugu Hayashi

    (Center for Sustainable Development and Global Smart City, Chubu University, Kasugai 487-8501, Japan)

  • Boonserm Kijsirikul

    (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

  • Yuji Iwahori

    (Department of Computer Science, Chubu University, Kasugai 487-8501, Japan)

Abstract

Understanding the quality of life related to transportation plays a crucial role in enhancing commuters’ quality of life, particularly in daily trips. This study explores the spatial effects of built environment on quality of life related to transportation (QoLT) through the combination of GIS application and deep learning based on a questionnaire survey by focusing on a case study in Sukhumvit district, Bangkok, Thailand. The Geographic Information System (GIS) was applied for spatial analysis and visualization among all variables through a grid cell (500 × 500 sq.m.). In regard to deep learning, the semantic segmentation process that the model used in this research was OCRNet, and the selected backbone was HRNet_W48. A quality-of-life-related transportation indicator (life satisfaction) was implemented through 500 face-to-face interviews and the data were collected by a questionnaire survey. Then, multinomial regression analysis was performed to demonstrate the significant in positive and negative aspects of independent variables (built environment) with QoLT variables at a 0.05 level of statistical significance. The results revealed the individuals’ satisfaction from a diverse group of people in distinct areas or environments who consequently perceived QoLT differently. Built environmental factors were gathered by application of GIS and deep learning, which provided a number of data sets to describe the clusters of physical scene characteristics related to QoLT. The perception of commuters could be translated to different clusters of the physical attributes through the indicated satisfaction level of QoLT. The findings are consistent with the physical characteristics of each typological site context, allowing for an understanding of differences in accessibility to transport systems, including safety and cost of transport. In conclusion, these findings highlight essential aspects of urban planning and transport systems that must consider discrepancies of physical characteristics in terms of social and economic needs from a holistic viewpoint. A better understanding of QoLT adds important value for transportation development to balance the social, economic, and environmental levels toward sustainable futures.

Suggested Citation

  • Pawinee Iamtrakul & Sararad Chayphong & Pittipol Kantavat & Yoshitsugu Hayashi & Boonserm Kijsirikul & Yuji Iwahori, 2023. "Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2785-:d:1056814
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    References listed on IDEAS

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    Cited by:

    1. Milan Lalić & Milka Bubalo Živković & Bojan Đerčan & Dragana Tekić, 2024. "Quality of Life as a Limiting Factor in the Development of the Region along the Great Bačka Canal (Serbia)," Sustainability, MDPI, vol. 16(6), pages 1-34, March.
    2. Pawinee Iamtrakul & Sararad Chayphong & Alexander M. Crizzle, 2023. "Impact of Spatial Configuration on Promoting Lifelong Learning Development in Pathum Thani, Thailand," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    3. Pawinee Iamtrakul & Sararad Chayphong & Pittipol Kantavat & Kazuki Nakamura & Yoshitsugu Hayashi & Boonserm Kijsirikul & Yuji Iwahori, 2024. "Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images," Sustainability, MDPI, vol. 16(4), pages 1-20, February.
    4. Pawinee Iamtrakul & Sararad Chayphong & Derlie Mateo-Babiano, 2023. "The Transition of Land Use and Road Safety Studies: A Systematic Literature Review (2000–2021)," Sustainability, MDPI, vol. 15(11), pages 1-20, May.

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