A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Detecting older pedestrians and aging-friendly walkability using computer vision technology and street view imagery
Abstract As an emerging and freely available urban big data, Street View Imagery (SVI) has proven to be a useful resource to examine various urban phenomena in human behavior, the built environment and their interactions. However, due to technical limitations, previous studies often focused on general pedestrians and ignored certain population subgroups such as older adults. In this study, we develop an innovative method for detecting older pedestrians using SVI. We adopted transfer learning to train a model which can accurately detect older pedestrians on SVI with an accuracy of 87.1%. Using Hong Kong as a case study, we created a dataset consisting of 72,689 street view panoramas and detected 7763 older pedestrians and 29,231 non-older pedestrians. We further visualized the distribution of detected older pedestrians and found a significant spatial discrepancy between older pedestrians and residential population of older adults. To account for this spatial discrepancy, this study proposed a novel index to assess pedestrian demand and walking environment based on the ratio of the number of pedestrians and the residential population. We also found pedestrian demand assessed with this index has a stronger correlation with the built environment compared with population-level travel survey. This novel approach can be used to assess pedestrian demand for older adults, as well as aging-friendly walking environment.
Highlights An approach for assessing aging-friendly environments at large scale was developed. The approach was automatic, cost-efficient and accurate. Transfer learning was employed to train the model. Spatial discrepancy between older pedestrians and residents was observed. A new index was proposed and validated for assessing aging-friendly walkability.
Detecting older pedestrians and aging-friendly walkability using computer vision technology and street view imagery
Abstract As an emerging and freely available urban big data, Street View Imagery (SVI) has proven to be a useful resource to examine various urban phenomena in human behavior, the built environment and their interactions. However, due to technical limitations, previous studies often focused on general pedestrians and ignored certain population subgroups such as older adults. In this study, we develop an innovative method for detecting older pedestrians using SVI. We adopted transfer learning to train a model which can accurately detect older pedestrians on SVI with an accuracy of 87.1%. Using Hong Kong as a case study, we created a dataset consisting of 72,689 street view panoramas and detected 7763 older pedestrians and 29,231 non-older pedestrians. We further visualized the distribution of detected older pedestrians and found a significant spatial discrepancy between older pedestrians and residential population of older adults. To account for this spatial discrepancy, this study proposed a novel index to assess pedestrian demand and walking environment based on the ratio of the number of pedestrians and the residential population. We also found pedestrian demand assessed with this index has a stronger correlation with the built environment compared with population-level travel survey. This novel approach can be used to assess pedestrian demand for older adults, as well as aging-friendly walking environment.
Highlights An approach for assessing aging-friendly environments at large scale was developed. The approach was automatic, cost-efficient and accurate. Transfer learning was employed to train the model. Spatial discrepancy between older pedestrians and residents was observed. A new index was proposed and validated for assessing aging-friendly walkability.
Detecting older pedestrians and aging-friendly walkability using computer vision technology and street view imagery
Liu, Dongwei (author) / Wang, Ruoyu (author) / Grekousis, George (author) / Liu, Ye (author) / Lu, Yi (author)
2023-08-11
Article (Journal)
Electronic Resource
English
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