From Data to Policy: Weather Resilience Indices for Climate-Adaptive Shared Mobility Planning

Abstract

Designing climate-resilient public transportation requires data-driven tools that can identify vulnerabilities, measure infrastructure performance, and support targeted policy decisions, yet such tools remain scarce. This study addresses that gap by developing novel indices for climate-adaptive shared mobility planning: the Weather Resilience Transportation Index (WRTI), which quantifies station-level sensitivity to climate stress, and the Weather Resilience Infrastructure Factor (WRIF), which measures infrastructure-mediated resilience under varying weather conditions. Both indices are developed within the Hybrid Dynamical Systems Thinking Approach (HDSTA), integrating systems thinking, Object-Process Methodology (OPM), machine learning (ML), and causal inference. Applied to two contrasting case studies: bus ridership along Israel's Highway 2 and bike-sharing in New York City's Citi Bike system, the framework reveals nonlinear, location-specific climate responses and quantifies the ridership and resilience returns of infrastructure investment. The results demonstrate how combining data science with system-level thinking can translate complex climate-behavior interactions into actionable, evidence-based policy.