A Data-Driven Approach for the Analysis of Ridership Fluctuations in Transit Systems

This study focuses on identifying critical components within urban public transportation networks, particularly in the context of fluc-tuating demand and potential pandemic scenarios. By employing advanced agent-based simulations, we analyzed passenger interac-tions and ridership patterns across the San Francisco Bay Area’s transit system. Key findings reveal specific transit stops and routes that are highly sensitive to changes in demand, often serving as bottlenecks or high-risk areas for the spread of infectious diseases.