skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

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

Sustainability, 2023-02, Vol.15 (3), p.2785 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su15032785

Full text available

Citations Cited by
  • Title:
    Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches
  • Author: Iamtrakul, Pawinee ; Chayphong, Sararad ; Kantavat, Pittipol ; Hayashi, Yoshitsugu ; Kijsirikul, Boonserm ; Iwahori, Yuji
  • Subjects: Artificial intelligence ; Built environment ; Central business districts ; Commuters ; Commuting ; Data processing ; Deep learning ; Economic aspects ; Environmental aspects ; Environmental factors ; Evaluation ; Geographic information systems ; Independent variables ; Influence ; Literature reviews ; Machine learning ; Physical characteristics ; Physical properties ; Quality of life ; Questionnaires ; Regression analysis ; Social aspects ; Spatial analysis ; Statistical analysis ; Surveys ; Transportation ; Transportation systems ; Urban environments ; Urban planning
  • Is Part Of: Sustainability, 2023-02, Vol.15 (3), p.2785
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2071-1050
    EISSN: 2071-1050
    DOI: 10.3390/su15032785
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait