Modeling the influence of restriction policies and perceived risk due to COVID-19 on daily activity scheduling

This study develops an Activity-Based Model (ABM) framework to provide a deeper understanding of how activity restriction policies and perceived risks influence human mobility and, consequently, disease transmission. We propose three main contributions: (i) the Activity-Based Restriction Model (ABRM) systematically implements various activity restriction policies, such as closures, curfews, and distance-based limitations, (ii) we introduce a dynamic programming algorithm to address computational intractability in large-scale scenarios, significantly reducing computation time, (iii) we build a Risk Perception Latent Variable Model to simulate how perceived risks influence individual scheduling behavior. By embedding this model into the ABRM, we create the Activity-Based Risk Perception Restriction Model (ABR²M), which captures the dynamic interplay between risk perception and activity scheduling given activity-restriction policies. This integrated approach provides a detailed evaluation of individual schedules, offering valuable insights for the development of informed transportation policies.