A hybrid optimization and microsimulation framework for simulating weekly travel activities

This paper introduces a simulation-based optimization framework to simulate weekly travel activities from single-day data, addressing the diminishing relevance of a "Typical Day." The framework builds on the integrated land use and energy modeling system (iTLE), combining long- and short-term decision components, a Markov Chain Monte Carlo (MCMC)-based activity generator, and an activity scheduler. The optimization model accounts for individual travel preferences through constraints, iteratively calibrating activity transition probabilities to match desired weekly travel patterns. Key activities simulated include in-home work (IHW), in-person work, shopping, eating out, and recreation. Calibration results show deviations of 19.0%, 16.4%, and 15.2% for IHW, in-person work, and shopping, respectively, with validation deviations of 4.3% for eating out and 2.3% for recreation. The model highlights distinct work patterns and commute behaviors, offering flexibility for post-pandemic transportation planning policies.