Inspecting climate impacts on city journey demand in Nanjing, China through the COVID-19 pandemic

Accurately characterizing variation in public transport demand is essential for city visitors administration, particularly when climate circumstances and public-health disruptions collectively reshape journey patterns. Using month-to-month transportation statistics and climate observations for Nanjing, China, from February 2019 to December 2022, this research examines associations between climate circumstances and passenger circulation throughout buses, taxis, and the Rail Transit System (RTS) through the COVID-19 interval. Random Forest (RF) and Shapley Additive Explanations (SHAP)-based interpretation have been used as exploratory instruments to look at the relative associations of climate variables with passenger-flow variation. The outcomes present that RTS remained the dominant journey mode in Nanjing, adopted by buses and taxis, and that passenger circulation for all three modes declined sharply on the onset of the pandemic earlier than progressively recovering. During the research interval, temperature usually elevated, whereas different climate variables confirmed adjustments in various instructions. Although the significance rankings derived from the RF and SHAP strategies usually are not solely constant, each establish temperature and precipitation as essential climate components influencing adjustments in visitors demand. In addition, the pandemic was related to a marked shift in modal construction: earlier than the pandemic, passenger-flow shares have been comparatively balanced throughout modes, whereas afterward taxi and RTS accounted for a bigger share and bus ridership declined. These findings present could provide a preliminary reference for adaptive transport planning.