@article{2574c52b02fd4b1391a120d877dbfc98,
title = "Exploring built environment correlates of older adults{\textquoteright} walking travel from lifelogging images",
abstract = "Utilising time-stamped lifelogging images collected from a sample of 30 older adults in three neighbourhoods of Singapore, this study explores older adults{\textquoteright} daily walking travel patterns and their associations with neighbourhood-level built environment features. The visual lifelogging method uses a wearable camera to automatically record individual activities from a “first-person” perspective and capture novel information of environmental features encountered by individuals in real time. The findings reveal that older participants in the study areas, on average, take about 4 walking trips per day with an average trip length of about 15 minutes. Neighbourhood facilities including public open spaces, senior activity centres, and food courts are the most visited destinations while urban design features such as covered walkways, tree shades and street furniture are frequently encountered during walking trips.",
keywords = "Built environment, Older adults, Singapore, Visual lifelogging, Walking travel",
author = "Yuting Hou and Adithi Moogoor and Anna Dieterich and Siqi Song and Belinda Yuen",
note = "Funding Information: This research is supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister's Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2NICTDF1-2017-2). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Singapore Ministry of National Development and National Research Foundation, Prime Minister's Office, Singapore. We are deeply grateful to all research participants for generously giving their time and for sharing their personal experiences. We duly acknowledge the following for their generous support and assistance: Dr Hyowon Lee who helped us select the lifelogging camera and provided technical assistance; Dr Ngai-Man Cheung who guided the development of image tagging software; master student Muhammad Hafiz Bin Thaha who tested the effectiveness of using machine learning algorithm for scene (label) recognition; research assistant Rochelle Chua who participated in data collection, cleaning and maintenance; student assistants Doan Thanh, You Song Shan and Jinjing Jiang who developed image tagging software, and student helpers who helped with data entry and image tagging. This material is based on research/work supported by Singapore's Ministry of National Development and National Research Foundation under the Land and Liveability National Innovation Challenge Funding Programme (grant number L2NICTDF1-2017-2). The research protocol was approved by the Singapore University of Technology and Design Institutional Review Board. Funding Information: This material is based on research/work supported by Singapore{\textquoteright}s Ministry of National Development and National Research Foundation under the Land and Liveability National Innovation Challenge Funding Programme (grant number L2NICTDF1-2017-2 ). Funding Information: This research is supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister{\textquoteright}s Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2NICTDF1-2017-2). Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",
year = "2021",
month = jul,
doi = "10.1016/j.trd.2021.102850",
language = "English",
volume = "96",
journal = "Transportation Research Part D: Transport and Environment",
issn = "1361-9209",
publisher = "Elsevier Ltd",
}