Riding through the heat: Assessing heat health risk of cycling based on diurnal temperature and shared-bike big data

Fine-scale heat health risks assessment is critical to improve resilience to global warming and extreme heat events. This study proposed a framework to estimate heat health risks of cyclists using massive bike-sharing trajectories and diurnal dynamic temperature data. This study proposed two indicators, accumulated heat health risks (CycHeat) and accumulated excessive heat health risks (CycExcessHeat), to estimate the heat health risks of bike-sharing cyclists in Beijing, Shanghai, and Xiamen based on massive GPS trajectories and diurnal dynamic temperature data. Results show that there is no significant difference in terms of the CycHeat between the morning peak and evening peak in both three cities. While the CycExcessHeat in the morning peak is significantly higher than that in the evening peak in Beijing, Shanghai, and Xiamen (p < 0.05). That is, the indicator that focuses on extreme heat is more reflective of spatiotemporal heterogeneity in cyclists' heat health risks. Finally, the relationship between CycExcessHeat and the built environment was regressed, and the model results align with the literature focusing on cycling frequency. This study provided a framework to estimate heat health risks of outdoor mobility, which provides management implications for transportation authorities and urban planning in the context of SDGs 3 and 11.