Synthetic multi-day activity-travel schedules for Swedish residents

Background 

The activity-travel schedules were created using a novel generative model that synthesizes individuals' average weekday activity-travel schedules from mobile phone application data. Mobile data provides geographically and population-wise extensive observations over extended periods, offering valuable insights into individuals' whereabouts. However, these datasets often include sampling biases in the population coverage and individual-level data sparsity due to intermittent and irregular phone application activities, from which the underlying geolocation data were passively collected.  

The generative model combines mobile data with the Swedish national travel survey [1]. The model employs state-of-the-art primary activity identification methods to infer individuals’ primary activity locations, i.e., home and work/school snapped to buildings. The proposed model can generate multiple schedules for each individual, showing activity sequences, types, start/end times and locations, incorporating daily variability in specific schedule attributes. At the individual level, variations occur across all elements of activity schedules, i.e., activity sequences, type, start/end times, and other activity locations, while maintaining the residential and workplace locations. Moreover, the model calculates a weight for each individual based on their residential location and inferred employment status, addressing sampling biases and ensuring a representative sample of the Swedish population.  

The performance of the generative model is evaluated by comparing its synthesized activity-travel schedules with those from the SySMo model [2], large-scale agent-based model of Sweden and with underlying travel survey data. The results demonstrate that the proposed model effectively addresses biases and sparsity in mobile phone application data, resulting in realistic and reliable activity-travel schedules. The pre-print paper "Mobile Phone Application Data for Activity Plan Generation" details the model's methodology and evaluation.