A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Associations of Built Environments with the Spatiotemporal Patterns of Older Adults
With the accelerated aging process, the increased travel for older adults has emerged as a crucial means to promote social participation. In order to better understand their travel characteristics, this study grounds in the spatial–temporal geographic information theory, investigating the travel patterns of older adults. Specifically, we introduce an nonnegative Candecomp/Parafac (NCP)-CatBoost comprehensive analysis framework to explore their nonlinear relationships between the built environment and travel behavior among older adults, using Xi'an as a case study to validate the feasibility of the proposed framework. The study's findings reveal the following: (1) Five representative spatial–temporal travel patterns are identified, namely, the high-intensity morning-peak, moderate-intensity morning-peak, moderate-intensity no-peak, high-intensity evening-peak, and low-intensity no-peak patterns. These patterns show spatial clustering, with similar travel behaviors concentrated in specific areas; (2) Socioeconomic variables are primary determinants of ridership across all patterns, while land-use factors (recreational density and land-use mix) significantly affect specific travel patterns such as a high-intensity evening-peak pattern; and (3) Built environment variables show distinct threshold effects on travel patterns. Notably, the threshold for shopping density is 8 units/km2 in the low-intensity no-peak pattern, whereas it increases to 24 units/km2 in the high-intensity evening-peak pattern. These findings highlight that the NCP-CatBoost framework could capture the complex dynamics between different travel patterns and the built environment, providing valuable insights for policymakers to implement targeted built environment interventions that meet age-friendly travel needs.
Associations of Built Environments with the Spatiotemporal Patterns of Older Adults
With the accelerated aging process, the increased travel for older adults has emerged as a crucial means to promote social participation. In order to better understand their travel characteristics, this study grounds in the spatial–temporal geographic information theory, investigating the travel patterns of older adults. Specifically, we introduce an nonnegative Candecomp/Parafac (NCP)-CatBoost comprehensive analysis framework to explore their nonlinear relationships between the built environment and travel behavior among older adults, using Xi'an as a case study to validate the feasibility of the proposed framework. The study's findings reveal the following: (1) Five representative spatial–temporal travel patterns are identified, namely, the high-intensity morning-peak, moderate-intensity morning-peak, moderate-intensity no-peak, high-intensity evening-peak, and low-intensity no-peak patterns. These patterns show spatial clustering, with similar travel behaviors concentrated in specific areas; (2) Socioeconomic variables are primary determinants of ridership across all patterns, while land-use factors (recreational density and land-use mix) significantly affect specific travel patterns such as a high-intensity evening-peak pattern; and (3) Built environment variables show distinct threshold effects on travel patterns. Notably, the threshold for shopping density is 8 units/km2 in the low-intensity no-peak pattern, whereas it increases to 24 units/km2 in the high-intensity evening-peak pattern. These findings highlight that the NCP-CatBoost framework could capture the complex dynamics between different travel patterns and the built environment, providing valuable insights for policymakers to implement targeted built environment interventions that meet age-friendly travel needs.
Associations of Built Environments with the Spatiotemporal Patterns of Older Adults
J. Urban Plann. Dev.
Li, Chenguang (author) / Gao, Jie (author) / Liu, Wenyu (author) / Chen, Hong (author)
2025-06-01
Article (Journal)
Electronic Resource
English