Approaches for Human Mobility Data Generation:Research Progress and Trends
Human mobility data play a crucial role in many real-world applications such as infectious diseases,transportation,and public safety.The development of modern Information and Communication Technologies(ICT)has made it easier to collect large-scale individual-level human mobility data,however,the availability and usability of the raw data are still significantly limited due to privacy concerns,as well as issues of data redundancy,missing,and noise.Generating synthetic human mobility data through modeling approaches to statistically approximate the real data is a promising solution.From the data perspective,the generated human mobility data can serve as a substitute for real data,mitigating concerns about personal privacy and data security,and enhance the low-quality real data.From the modeling perspective,the constructed models for human mobility data generation can be used for scenario simulations and mechanism exploration.The human mobility data generation tasks include individual trajectory data generation and collective mobility data generation,and the research methods primarily consist of mechanistic models and machine learning models.This article firstly provides a systematic review of the research progress in human mobility data generation and then summarizes its development trends and challenges.It can be observed that mechanistic-model-based methods are predominantly studied in the field of statistical physics,while machine-learning-based methods are primarily studied in the field of computer science.Although the two types of models have complementary advantages,they are still developing independently.The article suggests that future research in human mobility data generation should focus on:1)exploring and revealing the underlying mechanisms of human mobility behavior from a multidisciplinary perspective;2)designing hybrid approaches by coupling machine learning and mechanistic models;3)leveraging cutting-edge generative Artificial Intelligence(AI)and Large Language Model(LLM)technologies;4)improving the models'spatial generalization and transfer-learning capabilities;5)controlling the costs of model training and implementation;and 6)designing reasonable evaluation metrics and balancing data utility with privacy-preserving effectiveness.The article asserts that human mobility processes are typical phenomenon of human-environment interactions.On the one hand,research in Geographic Information Science(GIS)field should integrate with theories and technologies from other disciplines such as computer science,statistical physics,complexity science,transportation,and others.While on the other hand,research in GIS field should harness the unique characteristics of GIS by explicitly incorporating geographic spatial effects,including spatial dependency,distance decay,spatial heterogeneity,scale,and more into the modeling process to enhance the rationality and performance of the human mobility data generation models.
human mobility datasynthetic datatrajectory generationmachine learningmechanical modelgenerative AIprivacy-preservingGeospatial Artificial Intelligence(GeoAI)
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