Abstract
Modeling the movements of individual and populations, and generating synthetic spatiotemporal trajectory data play an important role in lots of (privacy-aware) analysis and applications, such as urban planning and route navigation. A key challenge in trajectory generation is to best capture the basic characteristics of the long sequences of location points. This is non-trivial considering the inherent sequentiality and high-dimensionality of trajectory data. This paper presents TS-TrajGAN, a two-stage model to generate spatiotemporal trajectory data by combining a Generative Adversarial Network (GAN) and a conditional GAN. We train the GAN of stage I to simulate the distribution of the initial trajectory segments such that the basic characteristics of the length-limited initial trajectory segments can be well depicted. In stage II, the conditional GAN is used to predict the next location point for the current generated trajectory and preserve the variability in individuals’ mobility. In addition, a predictor network is added to the GAN of stage I for trajectory length prediction. Experiments on a real-world taxi dataset demonstrate that TS-TrajGAN is not only able to generate trajectories that have similar characteristics with the real ones, but also outperforms the state-of-the-art methods in terms of data utility. Our code is available at https://github.com/kfZhao726/TS-TrajGAN.
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Zhao, K., Wang, N. (2024). Generating Spatiotemporal Trajectories with GANs and Conditional GANs. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_32
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DOI: https://doi.org/10.1007/978-981-99-8126-7_32
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