System for Information-Driven Modeling of Urban Activity-Travel Routines of Generic Households

Activity-Based Models (ABMs) are powerful tools for travel demand forecasting. However, most existing operational ABMs are primarily static. They typically simplify weekly behavior into an "average day" and often predict only the main mode of a tour, overlooking modal chains. Additionally, population synthesis is usually conducted cross-sectionally, which risks ignoring state dependencies in life event predictions. The SIMURGH model advances the field by introducing a dynamic approach: it predicts the full modal chain, including access and egress modes; it replaces the "average day" with a complete weekly agenda; and it develops a dynamic synthetic population that captures temporal changes.