SNU Big Data Knowledge Information Platform
Full Menu
AI
Your Browsing History
15 More
loading...
Report registration
Paper List
해석가능한 기계학습을 활용한 보행목적별 보행만족도 영향요인 분석
Author
박준상, 이수기
Journal Title
국토계획
Publication Year
2022
Topics
Summary
This study investigates the factors influencing walking satisfaction by walking purpose using an interpretable machine learning model. The machine learning model demonstrates higher explanatory power than conventional models and analyzes nonlinear relationships to derive policy implications for enhancing walking satisfaction. Interaction effects of variables are analyzed using the Partial Dependence Plot method to contribute to the creation of a pedestrian-friendly urban environment.
Related Content
LikeSNU analyzes your query semantically to recommend related resources.
-
Public transportation quality of service : factors, models, and applications
-
보행자를 위한 도시설계 =
-
Pedestrian behavior : models, data collection and applications
-
Accessibility and spatial interaction
-
Data-driven analytics for sustainable buildings and cities : from theory to application
-
Machine learning and the city : applications in architecture and urban design
-
The walkable city : dimensions of walking and overlapping walks of life
-
The structure and dynamics of cities : urban data analysis and theoretical modeling
-
AI 도시교통과 모빌리티
-
대규모 상업시설물의 환경특성과 길찾기
-
Is transport infrastructure effective? : transport infrastructure and accessibility : impacts on the space economy
-
Geospatial analysis and modelling of urban structure and dynamics
-
Regional development in an age of structural economic change
-
데이터 시각화와 자료분석 =
-
매개효과와 조절효과 =
-
Accessibility, equity and efficiency : challenges for transport and public services
-
Understanding mobilities for designing contemporary cities
-
Contemporary urban planning
-
생성형 AI 도시 읽기 : 미래도시 라이프스타일과 함께
-
Self-adaptive systems for machine intelligence
Previous
Next
Previous
Next