Publication detail

Revealing relationships between levels of air quality and walkability using explainable artificial intelligence techniques

Fan Yee Van, M.Phil. Ph.D. Choi Minje Jo Joonsik Kwak Juhyeon Lee Seungjae

English title

Revealing relationships between levels of air quality and walkability using explainable artificial intelligence techniques

Type

WoS Article

Language

en

Original abstract

Based on the global interest in environmental and health issues related to air pollution, this study addresses the impact of air quality on walking and related factors in cities. This study analyzes the impact of air quality on pedestrian volume in Seoul, Korea, and the relationship between these two variables. In this study, an Artificial Intelligence model was first built to predict pedestrian volume using various urban environmental variables. Then, using Explainable Artificial Intelligence techniques, various factors affecting pedestrian volume were post-analyzed and the interaction between pedestrian volume and air quality was identified. The results of the study show that air quality indicators have a high variable importance in predicting pedestrian volume, and when the indicators improve above a certain level, pedestrian volume is rapidly activated. In addition, the concentration of fine dust does not have a significant effect on the increase in pedestrian volume on weekdays and in urban centers where essential travel occurs, whereas in neighborhood parks, pedestrian volume elastically decreased due to the deterioration of air quality, and this phenomenon was more pronounced when the fine dust rating was downgraded. Finally, the sensitivity of walking variation by air quality was analyzed in consideration of population characteristics in neighborhood parks. In general, it was confirmed that women were more vulnerable to air quality than men, and young adults were relatively more vulnerable to air quality than children and the elderly in the age group, and this difference appeared differently depending on regional characteristics.

Keywords in English

Air quality, Walkability, XAI, Population characteristics, Net zero, Sustainable transport

Released

2025-12-01

Publisher

Springer Nature

Journal

Clean Technologies and Environmental Policy

Volume

27

Number

12

Pages from–to

8623–8639

Pages count

17

BIBTEX


@article{BUT201442,
  author="{} and  {} and  {} and Yee Van {Fan} and  {}",
  title="Revealing relationships between levels of air quality and walkability using explainable artificial intelligence techniques",
  journal="Clean Technologies and Environmental Policy",
  year="2025",
  volume="27",
  number="12",
  pages="8623--8639",
  doi="10.1007/s10098-024-03012-9",
  issn="1618-954X",
  url="https://link.springer.com/article/10.1007/s10098-024-03012-9"
}