Quantifying the impact of transportation diversity on human mobility resilience during extreme disasters: A case study of NYC during hurricane Ida

This study proposes an innovative multi-modal and spatially explicit framework to quantify urban mobility resilience during extreme weather events, demonstrated through the case study of Hurricane Ida’s 2021 impact on New York City. The framework integrates dynamic baseline forecasting using the Prophet time-series model, enabling the precise characterization of expected mobility patterns under normal conditions. By comparing observed mobility with these dynamic baselines across subway, taxi, and bike-sharing systems, it effectively identifies the magnitude and duration of disruptions at a zone-level scale. Results revealed substantial differences by transport mode: subways experienced severe, prolonged disruptions due to fixed infrastructure, whereas taxis and bike-sharing systems recovered rapidly, acting as effective substitutes. Spatial analysis high-lighted central areas as more resilient due to greater transportation diversity and infrastructure density, while peripheral neighborhoods faced longer disruptions. Transportation diversity was identified as a significant predictor of resilience, yet its impact varied according to local socio-economic and infrastructural contexts. Employing Multiscale Geographically Weighted Regression (MGWR), the study showed that diversity yielded the greatest resilience benefits in neighborhoods characterized by higher incomes, greater connectivity, and lower social vulnerability. In contrast, socio-economic and environmental constraints in other areas limited residents’ ability to utilize available transportation options fully. The proposed framework offers practical guidance for urban planners and policymakers on identifying local vulnerabilities, prioritizing equitable infrastructure investments, and enhancing the adaptive capacity of transportation systems in the face of increasing climate-related risks.