Hyperlocal heat stress around bus stops in Philadelphia: Insights from spatio-temporal microclimate modeling and explainable AI

The Urban Heat Island (UHI) effect significantly impacts public transit users, particularly those waiting at bus stops, where prolonged exposure to extreme heat poses health risks. Despite increasing attention to climate resilience, limited research has focused on hyperlocal, pedestrian-level thermal stress at bus stops or its relationship with the surrounding urban environment. To address this gap, we generated hourly 1-meter resolution Universal Thermal Climate Index (UTCI) maps for Philadelphia using high-resolution, multi-source geospatial data and microclimate modeling, capturing detailed summer daytime spatio-temporal heat stress patterns around more than 8,000 bus stops. We further developed an explainable machine learning framework, combining Random Forest (RF) and SHAP analysis to uncover complex, nonlinear relationships and threshold effects between heat stress and both built environment and socioeconomic variables. Key findings include: (1) Significant spatio-temporal variation in heat stress, with consistently high levels at midday across the city; (2) Higher heat stress around bus stops located in low-income neighborhoods, while more affluent areas (e.g., higher median household value) exhibit reduced thermal exposure; (3) Green View Index (GVI) and Enclosure emerged as the most effective heat-mitigating features, and (4) complex threshold effects across key urban indicators highlight the importance of targeted and equitable interventions to reduce heat stress in vulnerable areas.