File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.jtrangeo.2023.103698
- Scopus: eid_2-s2.0-85174358580
- WOS: WOS:001103989200001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Examining non-linear relationship between streetscape features and propensity of walking to school in Hong Kong using machine learning techniques
Title | Examining non-linear relationship between streetscape features and propensity of walking to school in Hong Kong using machine learning techniques |
---|---|
Authors | |
Keywords | Active school commuting Machine learning Non-linearity Random forest Streetscape feature Travel behavior |
Issue Date | 2023 |
Citation | Journal of Transport Geography, 2023, v. 113, article no. 103698 How to Cite? |
Abstract | Active school commuting makes a vital contribution to physical activity, thus improving the health and well-being for children and adolescents. The built environment is widely acknowledged as a factor that can affect travel behavior. However, few studies assess the influence of micro-level streetscape features on active school commuting using street view images. Additionally, existing research often presupposes that the relationship between built environment and walking to school is linear or generalized linear. Using data from the 2016 census and street view images in Hong Kong, this paper investigates the non-linear relationship between the micro-level streetscape features and propensity of walking to school with several machine learning methods. A refined evaluation of streetscape features is conducted through semantic segmentation, object detection and perceptual color analysis. The results show that the non-linear model facilitates a deeper understanding of the genuine relationship between streetscape features and propensity of walking to school. Grass view index, mean saturation of buildings and number of traffic lights have greater significance in predicting walking propensity. Moreover, some of the streetscape features have salient threshold effects, indicating that environmental interventions would only be effective within a specific range. These findings can provide nuanced and fine-grained guidance for building a walkable, children-friendly and sustainable city. |
Persistent Identifier | http://hdl.handle.net/10722/336396 |
ISSN | 2023 Impact Factor: 5.7 2023 SCImago Journal Rankings: 1.791 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, Fangning | - |
dc.contributor.author | Li, Wenjing | - |
dc.contributor.author | Qiu, Waishan | - |
dc.date.accessioned | 2024-01-15T08:26:30Z | - |
dc.date.available | 2024-01-15T08:26:30Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Journal of Transport Geography, 2023, v. 113, article no. 103698 | - |
dc.identifier.issn | 0966-6923 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336396 | - |
dc.description.abstract | Active school commuting makes a vital contribution to physical activity, thus improving the health and well-being for children and adolescents. The built environment is widely acknowledged as a factor that can affect travel behavior. However, few studies assess the influence of micro-level streetscape features on active school commuting using street view images. Additionally, existing research often presupposes that the relationship between built environment and walking to school is linear or generalized linear. Using data from the 2016 census and street view images in Hong Kong, this paper investigates the non-linear relationship between the micro-level streetscape features and propensity of walking to school with several machine learning methods. A refined evaluation of streetscape features is conducted through semantic segmentation, object detection and perceptual color analysis. The results show that the non-linear model facilitates a deeper understanding of the genuine relationship between streetscape features and propensity of walking to school. Grass view index, mean saturation of buildings and number of traffic lights have greater significance in predicting walking propensity. Moreover, some of the streetscape features have salient threshold effects, indicating that environmental interventions would only be effective within a specific range. These findings can provide nuanced and fine-grained guidance for building a walkable, children-friendly and sustainable city. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Transport Geography | - |
dc.subject | Active school commuting | - |
dc.subject | Machine learning | - |
dc.subject | Non-linearity | - |
dc.subject | Random forest | - |
dc.subject | Streetscape feature | - |
dc.subject | Travel behavior | - |
dc.title | Examining non-linear relationship between streetscape features and propensity of walking to school in Hong Kong using machine learning techniques | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.jtrangeo.2023.103698 | - |
dc.identifier.scopus | eid_2-s2.0-85174358580 | - |
dc.identifier.volume | 113 | - |
dc.identifier.spage | article no. 103698 | - |
dc.identifier.epage | article no. 103698 | - |
dc.identifier.isi | WOS:001103989200001 | - |