Speaker
Description
The concept of the 15-minute living circle, which aims to provide residents with access to essential services within a 15-minute walk or bike ride, has gained significant traction as a sustainable urban development model. However, ensuring equitable access to transport services within these circles remains a critical challenge. This study leverages explainable machine learning techniques, specifically Random Forest (RF) and SHapley Additive exPlanations (SHAP), to assess transport service equity in Shenzhen, China, as a case study.
Using multi-source geospatial and socio-economic data, including public transport networks, population demographics, and land use patterns, we construct a comprehensive framework to evaluate accessibility and equity within 15-minute living circles. The RF model is employed to predict transport service accessibility for different demographic groups, while SHAP values are used to interpret the model and identify key factors influencing equity, such as public transport coverage, pedestrian infrastructure, and socio-economic disparities.
Preliminary results reveal significant spatial variations in transport service equity across Shenzhen, with underserved areas often correlated with lower income levels and inadequate infrastructure. The SHAP-based analysis highlights the importance of integrating community-centered transport policies and targeted investments to address these disparities. This study provides actionable insights for urban planners and policymakers to promote inclusive and sustainable transport systems within 15-minute living circles, contributing to the broader discourse on equitable urban development.
| Keywords | 15-minute living circle; Transport equity; Public transport accessibility |
|---|---|
| Best Congress Paper Award | Yes |
