Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models

Abstract

Human mobility data play a crucial role in many fields such as infectious diseases, transportation, and public safety. Although the development of Information and Communication Technologies (ICTs) has made it easy to collect individual-level positioning records, raw individual trajectory data are still limited in availability and usability due to privacy issues. Developing models to generate synthetic trajectories that are statistically close to the real data is a promising solution. This study proposed a novel trajectory generation method called Act2Loc (Activity to Location), which combined machine learning and mechanistic models. First, an activity-sequence generation model was constructed based on machine learning models (i.e. K-medoids and Transformer) to generate individual activity sequences aligning with human activity patterns. Then, a spatial-location selection model was proposed based on mechanistic models (e.g. Universal Opportunity model) to explicitly determine the specific locations of the activities in each generated sequence. Experimental results showed that compared to baselines based on purely machine learning or mechanistic models, Act2Loc can better reproduce the spatio-temporal characteristics of the real data, with additional advantage of low data requirements for training, proving its potential for generating synthetic trajectories in practice. This research offers new insights on knowledge-guided GeoAI models for human mobility.