Speaker
Description
With the advancement of child-friendly urban planning initiatives, the quality of Active School Travel Spaces (ASTSs) has an important impact on children’s healthy growth. However, existing research on evaluating the quality of school travel spaces predominantly relies on subjective experience, and a systematic, large-scale quantitative measurement framework hasn't been established. As a response to this situation, we systematically reviewed the literature and design guidelines related to child-friendly urban spaces and ASTS based on the hierarchy of children’s needs. Measurement indicators were systematically refined through Large Language Models (LLMs), and a quantitative "ASTS Index" framework suitable for large-scale measurement was developed. The measurement framework includes four dimensions: accessibility, safety, comfort, and convenience, with a total of 28 quantitative indicators.
This study takes street spaces along children's school routes and high-activity street spaces in the central urban area of Shanghai as research objects. The quality of streets is evaluated with the support of spatial design network analysis, multi-source big data and machine learning algorithms. Based on the evaluation results, each street segment is hierarchically clustered. Through experiments involving children's participation, the study reveals a high level of consistency between the results of the quantitative measurements and the subjective perceptions of the participants, providing preliminary evidence for the scientific validity and rationale of the analytical framework. This analytical approach makes a step forward for urban design practices through focusing on the assessment of ASTS quality and highlighting priority areas and short-board dimensions.
References
Lu, C., Yu, C., & Liu, X. (2024). Evaluating the Quality of Children’s Active School Travel Spaces and the Mechanisms of School District Friendliness Impact Based on Multi-Source Big Data. Land, 13(8), 1319.
Yang, Y., Wang, Q., Wu, D., Hang, T., Ding, H., Wu, Y., & Liu, Q. (2024). Constructing child-friendly cities: Comprehensive evaluation of street-level child-friendliness using the method of empathy-based stories, street view images, and deep learning. Cities, 154, 105385.
XU, S., ZHUANG, W. (2023). Research on the Quality Criteria of Street Space Affecting Children’s Hierarchy of School Travel Needs. Journal of Human Settlements in West China, 38(2), pp. 45-51.
| Keywords | Active school travel; Child- friendly; Multi-source big data; Machine learning |
|---|---|
| Best Congress Paper Award | No |
