Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: 136, P. 104400 - 104400
Published: Sept. 17, 2024
Language: Английский
Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: 136, P. 104400 - 104400
Published: Sept. 17, 2024
Language: Английский
Published: Jan. 1, 2025
Language: Английский
Citations
0Buildings, Journal Year: 2024, Volume and Issue: 14(11), P. 3332 - 3332
Published: Oct. 22, 2024
Advancements in analytical tools have facilitated numerous studies on perceived street quality. However, most focused limited aspects of quality, failing to capture a comprehensive perception. This study introduces quantitative approach holistically measure quality by integrating three key dimensions: visual perception, network accessibility, and functional diversity. Using Beijing Shanghai as case studies, we employed artificial neural networks analyze view images quantify the characteristics streets. Additionally, accessibility was assessed through spatial design analysis, diversity evaluated using entropy points interest (POIs) data. The evaluation results were combined analytic hierarchy process. reliability accuracy this method validated further testing. Our offers human-centered, large-scale measurement framework, providing valuable insights for urban renewal design.
Language: Английский
Citations
3Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: 136, P. 104400 - 104400
Published: Sept. 17, 2024
Language: Английский
Citations
0