Activity Trajectory Generation via Modeling Spatiotemporal Dynamics

Yuan Yuan · Jingtao Ding · Huandong Wang · Depeng Jin · Yong Li

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Abstract

Human daily activities, such as working, eating out, and traveling, play an essential role in contact tracing and modeling the diffusion patterns of the COVID-19 pandemic. However, individual-level activity data collected from real scenarios are highly limited due to privacy issues and commercial concerns. In this paper, we present a novel framework based on generative adversarial imitation learning, to generate artificial activity trajectories that retain both the fidelity and utility of the real-world data. To tackle the inherent randomness and sparsity of irregular-sampled activities, we innovatively capture the spatiotemporal dynamics underlying trajectories by leveraging neural differential equations. We incorporate the dynamics of continuous flow between consecutive activities and instantaneous updates at observed activity points in temporal evolution and spatial transformation. Extensive experiments on two real-world datasets show that our proposed framework achieves superior performance over state-of-the-art baselines in terms of improving the data fidelity and data utility in facilitating practical applications. Moreover, we apply the synthetic data to model the COVID-19 spreading, and it achieves better performance by reducing the simulation MAPE over the baseline by more than 50%. The source code is available online: https://github.com/tsinghua-fib-lab/Activity-Trajectory-Generation.

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References

2016Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts 2014Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs 2018DeepMove 2021An Empirical Study of Geographic User Activity Patterns in Foursquare 2012Mining User Mobility Features for Next Place Prediction in Location-Based Services 2019Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach 2016The TimeGeo modeling framework for urban mobility without travel surveys 2013What's Your Next Move: User Activity Prediction in Location-based Social Networks

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2023Practical Synthetic Human Trajectories Generation Based on Variational Point Processes 2024An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques 2023Learning to Simulate Daily Activities via Modeling Dynamic Human Needs 2023Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models 2024Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation

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