IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2847-d1057554.html
   My bibliography  Save this article

Quality of Life Prediction in Driving Scenes on Thailand Roads Using Information Extraction from Deep Convolutional Neural Networks

Author

Listed:
  • Kitsaphon Thitisiriwech

    (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Pathumwan, Bangkok 10330, Thailand)

  • Teerapong Panboonyuen

    (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Pathumwan, Bangkok 10330, Thailand)

  • Pittipol Kantavat

    (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Pathumwan, Bangkok 10330, Thailand)

  • Boonserm Kijsirikul

    (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Pathumwan, Bangkok 10330, Thailand)

  • Yuji Iwahori

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

  • Shinji Fukui

    (Faculty of Education, Aichi University of Education, Kariya 448-8542, Japan)

  • Yoshitsugu Hayashi

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

Abstract

In the modern era, urban design and sustainable development are vital topics for megacities, as they are important for the wellbeing of its residents. One of the effective key performance indices (KPIs) measuring the city plan’s efficiency in quantity and quality factors is Quality of Life (QOL), an index that policymakers can use as a critical KPI to measure the quality of urbanscape design. In the traditional approach, the researchers conduct the questionnaire survey and then analyze the gathered data to acquire the QOL index. The conventional process is costly and time-consuming, but the result of the evaluation area is limited. Moreover, it is difficult to embed in an application or system; we proposed artificial intelligence (AI) approaches to solve the limitation of the traditional method in Bangkok as a case study. There are two steps for our proposed method. First, in the knowledge extraction step, we apply deep convolutional neural networks (DCNNs), including semantic segmentation and object detection, to extract helpful information images. Second, we use a linear regression model for inferring the QOL score. We conducted various state-of-the-art (SOTA) models and public datasets to evaluate the performance of our method. The experiment results show that our novel approach is practical and can be considered for use as an alternative QOL acquisition method. We also gain some understanding of drivers’ insights from the experiment result.

Suggested Citation

  • Kitsaphon Thitisiriwech & Teerapong Panboonyuen & Pittipol Kantavat & Boonserm Kijsirikul & Yuji Iwahori & Shinji Fukui & Yoshitsugu Hayashi, 2023. "Quality of Life Prediction in Driving Scenes on Thailand Roads Using Information Extraction from Deep Convolutional Neural Networks," Sustainability, MDPI, vol. 15(3), pages 1-19, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2847-:d:1057554
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2847/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/3/2847/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ed Diener & Eunkook Suh, 1997. "Measuring Quality Of Life: Economic, Social, And Subjective Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 40(1), pages 189-216, January.
    2. Varameth Vichiensan & Kazuki Nakamura, 2021. "Walkability Perception in Asian Cities: A Comparative Study in Bangkok and Nagoya," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    3. Yoshitsugu Hayashi & Xianmin Mai & Hirokazu Kato, 2011. "The Role of Rail Transport for Sustainable Urban Transport," Transportation Research, Economics and Policy, in: Werner Rothengatter & Yoshitsugu Hayashi & Wolfgang Schade (ed.), Transport Moving to Climate Intelligence, chapter 0, pages 161-174, Springer.
    4. Helen Briassoulis, 2001. "Sustainable Development and its Indicators: Through a (Planner's) Glass Darkly," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 44(3), pages 409-427.
    5. Banister, David, 2008. "The sustainable mobility paradigm," Transport Policy, Elsevier, vol. 15(2), pages 73-80, 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. Marian Lubag & Joph Bonifacio & Jasper Matthew Tan & Ronnie Concepcion & Giolo Rei Mababangloob & Juan Gabriel Galang & Marla Maniquiz-Redillas, 2023. "Diversified Impacts of Enabling a Technology-Intensified Agricultural Supply Chain on the Quality of Life in Hinterland Communities," Sustainability, MDPI, vol. 15(17), pages 1-26, August.

    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. Leslie Gillespie‐Marthaler & Katherine Nelson & Hiba Baroud & Mark Abkowitz, 2019. "Selecting Indicators for Assessing Community Sustainable Resilience," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2479-2498, November.
    2. Nakamura, Kazuki & Hayashi, Yoshitsugu, 2013. "Strategies and instruments for low-carbon urban transport: An international review on trends and effects," Transport Policy, Elsevier, vol. 29(C), pages 264-274.
    3. Saujot, Mathieu & Lefèvre, Benoit, 2016. "The next generation of urban MACCs. Reassessing the cost-effectiveness of urban mitigation options by integrating a systemic approach and social costs," Energy Policy, Elsevier, vol. 92(C), pages 124-138.
    4. Busscher, Tim & Tillema, Taede & Arts, Jos, 2015. "In search of sustainable road infrastructure planning: How can we build on historical policy shifts?," Transport Policy, Elsevier, vol. 42(C), pages 42-51.
    5. Albino Prada-Blanco & Patricio Sanchez-Fernandez, 2017. "Empirical Analysis of the Transformation of Economic Growth into Social Development at an International Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(3), pages 983-1003, February.
    6. Thomas Vanoutrive & Ann Verhetsel, 2013. "Classifying transport studies using three dimensions of society: market structure, sustainability and decision making," Chapters, in: Thomas Vanoutrive & Ann Verhetsel (ed.), Smart Transport Networks, chapter 1, pages 1-8, Edward Elgar Publishing.
    7. Tornberg, Patrik & Odhage, John, 2018. "Making transport planning more collaborative? The case of Strategic Choice of Measures in Swedish transport planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 416-429.
    8. Tammaru, Tiit & Sevtsuk, Andres & Witlox, Frank, 2023. "Towards an equity-centred model of sustainable mobility: Integrating inequality and segregation challenges in the green mobility transition," Journal of Transport Geography, Elsevier, vol. 112(C).
    9. Mouratidis, Kostas & Ettema, Dick & Næss, Petter, 2019. "Urban form, travel behavior, and travel satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 306-320.
    10. Idiano D'Adamo & Massimo Gastaldi & Ilhan Ozturk, 2023. "The sustainable development of mobility in the green transition: Renewable energy, local industrial chain, and battery recycling," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(2), pages 840-852, April.
    11. Alvaro Rodriguez-Valencia & Hernan A. Ortiz-Ramirez, 2021. "Understanding Green Street Design: Evidence from Three Cases in the U.S," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    12. Gössling, Stefan, 2016. "Urban transport justice," Journal of Transport Geography, Elsevier, vol. 54(C), pages 1-9.
    13. Cavoli, Clemence, 2021. "Accelerating sustainable mobility and land-use transitions in rapidly growing cities: Identifying common patterns and enabling factors," Journal of Transport Geography, Elsevier, vol. 94(C).
    14. Allard, Ryan F. & Moura, Filipe, 2018. "Effect of transport transfer quality on intercity passenger mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 89-107.
    15. Agnieszka Wojewódzka-Wiewiórska & Anna Kłoczko-Gajewska & Piotr Sulewski, 2019. "Between the Social and Economic Dimensions of Sustainability in Rural Areas—In Search of Farmers’ Quality of Life," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    16. Romanika Okraszewska & Aleksandra Romanowska & Marcin Wołek & Jacek Oskarbski & Krystian Birr & Kazimierz Jamroz, 2018. "Integration of a Multilevel Transport System Model into Sustainable Urban Mobility Planning," Sustainability, MDPI, vol. 10(2), pages 1-20, February.
    17. Combs, Tabitha S., 2017. "Examining changes in travel patterns among lower wealth households after BRT investment in Bogotá, Colombia," Journal of Transport Geography, Elsevier, vol. 60(C), pages 11-20.
    18. Yixuan Liu & Liumeng Li & Guomei Miao & Xinyan Yang & Yinghui Wu & Yanling Xu & Yonghong Gao & Yongzhi Zhan & Yiwei Zhong & Shujuan Yang, 2021. "Relationship between Children’s Intergenerational Emotional Support and Subjective Well-Being among Middle-Aged and Elderly People in China: The Mediation Role of the Sense of Social Fairness," IJERPH, MDPI, vol. 19(1), pages 1-12, December.
    19. Emilio Colombo & Alessandra Michelangeli & Luca Stanca, 2014. "La Dolce Vita : Hedonic Estimates of Quality of Life in Italian Cities," Regional Studies, Taylor & Francis Journals, vol. 48(8), pages 1404-1418, August.
    20. Marco Grasso & Luciano Canova, 2008. "An Assessment of the Quality of Life in the European Union Based on the Social Indicators Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 87(1), pages 1-25, May.

    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:15:y:2023:i:3:p:2847-:d:1057554. 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.