The University of Tokyo · 환경과학
쿠마코시 유키세키 교수의 연구실은 도시 생태계와 도시 생활 질 향상을 위한 스마트 시티 기반 연구를 중심으로, 도시 녹화, 자율 공유 이동수단(SAV), 스트리트스케이프 디자인 등 도시 환경의 정량적 분석을 전문으로 합니다. 특히, GVI 기반 녹화 지표의 개선과 지역 특성에 맞는 지표 개발을 통해 도시 녹지의 공정한 평가 방법을 모색하고 있으며, 도시의 기능 다각화와 밀도 간 상관관계를 분석함으로써 도시 활성화 전략을 제안합니다. 연구는 실증 데이터와 딥러닝 기반 분석을 융합해 도시 계획의 과학적 근거를 제공합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Urban greenery is considered an important factor in sustainable development and people’s quality of life in the city. To account for urban green vegetation, Green View Index (GVI), which captures the visibility of greenery at street level, has been used. However, as GVI is point-based estimation, when aggregated at an area-level by mean or median, it is sensitive to the location of sampled sites, overweighing the values of densely located sites. To make estimation at area-level more robust, this
Shared autonomous vehicles (SAVs) can have significant impacts on the transport system and land use by replacing private vehicles. Sharing vehicles without drivers is expected to reduce parking demand, and as a side effect, increase congestion owing to the empty fleets made by SAVs picking up travelers and relocating. Although the impact may not be uniform over a region of interest owing to the heterogeneity of travel demand distribution and network configuration, few studies have debated such i
Urban greenery is considered an important factor in relation to sustainable development and people's quality of life in the city. Although ways to measure urban greenery have been proposed, the characteristics of each metric have not been fully established, rendering previous researches vulnerable to changes in greenery metrics. To make estimation more robust, this study aims to (1) propose an improved indicator of greenery visibility for analytical use (standardized GVI; sGVI), and (2) quantify
Shared autonomous vehicles (SAVs) can have significant impacts on the transport system and land use by replacing private vehicles. Sharing vehicles without drivers is expected to reduce parking demand, and as a side effect, increase congestion owing to the empty fleets made by SAVs picking up travelers and relocating. Although the impact may not be uniform over a region of interest owing to the heterogeneity of travel demand distribution and network configuration, few studies have debated such i
The disorder of urban streetscapes would negatively affect people's perception of their aesthetic quality. The presence of billboards on building facades has been regarded as an important factor of the disorder, but its quantification methodology has not yet been developed in a scalable manner. To fill the gap, this paper reports the performance of our deep learning model on a unique data set prepared in Tokyo to recognize the areas covered by facades and billboards in streetscapes, respectively
The diversity and density of urban functions have been known to affect urban vibrancy positively, but the relation between the two has not been empirically examined; if high density is associated with low diversity in an area, its vibrancy may not increase. To obtain a better understanding of the metabolism of cities and directions for urban planning interventions, this paper offers empirical evidence on the association between the diversity and density of urban functions in the Tokyo Metropolit
