Spatiotemporal Patterns and Built Environment Mechanisms of Taxi Travel Resilience Under Air Pollution: Evidence from Lanzhou, China

Abstract

Air pollution is an important factor affecting urban transportation performance and travel behavior, yet taxi travel resilience under polluted conditions remains underexplored. This study focuses on Lanzhou’s central urban area and integrates taxi trajectory data with AQI data from April 2025 to construct a taxi travel resilience indicator. K-means clustering is applied to identify areas with different resilience levels, followed by Probit models to examine the associations between built environment factors and taxi travel resilience across weekdays/weekends and all-day/peak-hour periods. The results show that: (1) air pollution significantly reduces taxi travel demand on weekdays, while its impact is weaker on weekends; (2) taxi travel resilience exhibits significant spatiotemporal heterogeneity; (3) bus stop density, road density, and population density are consistently and positively associated with taxi travel resilience across different temporal conditions; and (4) robustness tests using an alternative AQI threshold of 200 yield generally consistent results, although the substantially smaller number of polluted days limits the strength of the robustness assessment. This study advances understanding of taxi travel resilience under air pollution and provides insights for improving urban transportation resilience.