A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Examining the association between the built environment and pedestrian volume using street view images
Abstract Many studies have confirmed that the characteristics of the built environment affect individual walking behaviors. However, scant attention has been paid to population-level walking behaviors, such as pedestrian volume, because of the difficulty of collecting such data. We propose a new approach to extract citywide pedestrian volume using readily available street view images and machine learning technique. This innovative method has superior efficiency and geographic reach. In addition, we explore the associations between the extracted pedestrian volume and both macro- and micro-scale built environment characteristics. The results show that micro-scale characteristics, such as the street-level greenery, open sky, and sidewalk, are positively associated with pedestrian volume. Macro-scale characteristics, operationalized using the 5Ds framework including density, diversity, design, destination accessibility, and distance to transit, are also associated with pedestrian volume. Hence, to stimulate population-level walking behaviors, policymakers and urban planners should focus on the built environment intervetions at both the micro and macroscale.
Highlights Population-level walking behaviors were represented by street-level pedestrian volume at a large scale in Shanghai, China. Pedestrian volume and street-level built environment were extracted from street view images using machine learning. Both macro- and micro-scale built environment were simultaneously considered including 5D variables and street-level characteristics at a large scale. Macro-scale built environment measured by 5Ds framework were associated with pedestrian volume. Micro-scale built environment such as street-level greenery, open sky, and sidewalk were associated with pedestrian volume.
Examining the association between the built environment and pedestrian volume using street view images
Abstract Many studies have confirmed that the characteristics of the built environment affect individual walking behaviors. However, scant attention has been paid to population-level walking behaviors, such as pedestrian volume, because of the difficulty of collecting such data. We propose a new approach to extract citywide pedestrian volume using readily available street view images and machine learning technique. This innovative method has superior efficiency and geographic reach. In addition, we explore the associations between the extracted pedestrian volume and both macro- and micro-scale built environment characteristics. The results show that micro-scale characteristics, such as the street-level greenery, open sky, and sidewalk, are positively associated with pedestrian volume. Macro-scale characteristics, operationalized using the 5Ds framework including density, diversity, design, destination accessibility, and distance to transit, are also associated with pedestrian volume. Hence, to stimulate population-level walking behaviors, policymakers and urban planners should focus on the built environment intervetions at both the micro and macroscale.
Highlights Population-level walking behaviors were represented by street-level pedestrian volume at a large scale in Shanghai, China. Pedestrian volume and street-level built environment were extracted from street view images using machine learning. Both macro- and micro-scale built environment were simultaneously considered including 5D variables and street-level characteristics at a large scale. Macro-scale built environment measured by 5Ds framework were associated with pedestrian volume. Micro-scale built environment such as street-level greenery, open sky, and sidewalk were associated with pedestrian volume.
Examining the association between the built environment and pedestrian volume using street view images
Chen, Long (author) / Lu, Yi (author) / Ye, Yu (author) / Xiao, Yang (author) / Yang, Linchuan (author)
Cities ; 127
2022-05-01
Article (Journal)
Electronic Resource
English
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