Generating Realistic Individual Activity Schedules via Activity Location Allocation Based on Simulated Travel Times
For researchers and practitioners in urban planning and epidemiology who need realistic activity schedules without privacy-invasive data, this method improves travel time consistency but is tested only on dummy data.
The paper proposes a framework for generating realistic individual activity schedules that preserves travel times consistent with survey data, reducing the discrepancy between simulated and reported travel times by 52.2% through iterative refinement.
Individual level daily activity schedules are essential for a wide range of applications, including infectious disease control, urban transportation planning, and policy design. In practice, such schedules are typically generated by combining population data with travel survey data. These data sources are used because they are often publicly available, whereas observed individual activity schedules are difficult to obtain due to privacy concerns. However, because of the complexity of mobility modelling, it is difficult to generate realistic activity schedules that also preserve travel times consistent with those reported in travel surveys. To address this issue, we propose a framework for generating activity schedules that iteratively applies a dynamic programming method to allocate activity locations based on simulated travel times. Numerical experiments with dummy data show that the proposed method reduces the discrepancy between simulated travel times and those reported in travel surveys by 52.2% relative to the first iteration through iterative refinement.