Journal
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Volume 86, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2020.101563
Keywords
Built environment; Computer vision; Deep learning; Pedestrian choice
Ask authors/readers for more resources
The study uses pedestrian trajectories and built environment analysis to show that desirable streets provide better access to public amenities and have characteristics such as sinuosity and visual enclosure.
The experience of walking through a city is influenced by amenities and the visual qualities of its built environment. This paper uses thousands of pedestrian trajectories obtained from GPS signals to construct a desirability index for streets in Boston. We create the index by comparing the actual paths taken by pedestrians with the shortest path between any origin-destination pairs. The index captures pedestrians' willingness to deviate from their shortest path and provides a measure of the scenic and experience value provided by different parts of the city. We then use computer vision techniques combined with georeferenced data to measure the built environment of streets. We show that desirable streets have better access to public amenities such as parks, sidewalks, and urban furniture. They are also sinuous, visually enclosed, have less complex facades, and have more diverse business establishments. These results further our understanding of the value that the built environment brings to pedestrians, enhancing our capacity to design more lively and functional environments.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available