Abstract Extreme weather poses increasing challenges to urban transit systems, yet the resilience of subway ridership under such conditions remains insufficiently understood. This study develops an hour-specific vine copula framework for New York City subway ridership modeling, decomposing high-dimensional inter-station relationships into bivariate components while preserving non-linear and asymmetric dependencies. The methodology captures time-varying dependencies, generates realistic ridership distributions under diverse weather conditions, and enables quantitative assessment of ridership resilience to extreme events. Validation demonstrates strong performance, with 83 percent of scenarios achieving Kullback–Leibler divergence below 0.15. Results show a dynamic dependence structure across stations that varies under different environmental conditions. Results indicate that Manhattan core stations exhibit higher ridership resilience, whereas outer borough stations are more vulnerable. Heavy precipitation produces the most severe peak-hour impacts, while extreme cold primarily reduces off-peak ridership. For example, heavy precipitation during peak hours leads to a median 19.3 percent decline in Penn Station ridership (95% CI − 19.6, −3.4), whereas extreme heat during off-peak hours reduces Broadway/Jackson Heights ridership by a median 14.8 percent (95% CI − 31.4, −12.7). This framework provides a data-driven foundation for assessing ridership resilience and guiding climate adaptation and equitable transit investment in metropolitan systems.
Guo et al. (Wed,) studied this question.