Integrating non-traditional factors into a deterministic utility function for optimizing daily activity schedules

Activity-Based Models (ABMs) are critical for capturing the complex interconnectivity of daily scheduling and travel decisions. However, integrating optimization frameworks into ABMs presents significant challenges, as most existing approaches rely on the stochastic Random Utility Theory (RUT), which introduces substantial computational and operational complexity when applied to activity scheduling. To address these limitations, this study proposes a deterministic utility function, designed as an objective function, for activity schedule optimization. Moving beyond traditional time–cost factors, this utility synthesizes spatiotemporal constraints with non-traditional variables, including environmental impact, health benefit, and weather discomfort. We validate the utility approach through a comprehensive scenario analysis across four transport modes. The results demonstrate the model’s robustness in generating optimal schedules within both single and multi-modal contexts. By transitioning from simple route-finding to holistic multi-criteria optimization, this approach offers dual benefits: it serves as a sophisticated itinerary assistant for end-users and provides city planners with a best-case benchmark for evaluating system efficiency.