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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References
Berke, A., Doorley, R., Larson, K., Moro, E.: Generating synthetic mobility data for a real-istic population with RNNs to improve utility and privacy. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 964–967 (2022)
Blanco-Justicia, A., Jebreel, N.M., Manjón, J.A., Domingo-Ferrer, J.: Generation of synthetic trajectory microdata from language models. In: Domingo-Ferrer, J., Laurent, M. (eds.) Privacy in Statistical Databases. PSD 2022. Lecture Notes in Computer Science, vol. 13463, pp. 172–187. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13945-1_13
Kulkarni, V., Tagasovska, N., Vatter, T., Garbinato, B.: Generative models for simulating mobility trajectories. arXiv preprint arXiv:1811.12801 (2018)
Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds.) SSD 1999. LNCS, vol. 1651, pp. 147–164. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48482-5_11
Bindschaedler, V., Shokri, R.: Synthesizing plausible privacy-preserving location traces. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 546–563. IEEE (2016)
Shin, S., Jeon, H., Cho, C., Yoon, S., Kim, T.: User mobility synthesis based on generative adversarial networks: a survey. In: 2020 22nd International Conference on Advanced Com-munication Technology (ICACT), pp. 94–103. IEEE (2020)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Goodfellow, I.J., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Ouyang, K., Shokri, R., Rosenblum, D.S., Yang, W.: A non-parametric generative model for human trajectories. In: International Joint Conferences on Artificial Intelligence, vol. 18, pp. 3812–3817 (2018)
Cao, C., Li, M.: Generating mobility trajectories with retained data utility. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2610–2620 (2021)
Wang, X., Liu, X., Lu, Z., Yang, H.: Large scale GPS trajectory generation using map based on two stage GAN. J. Data Sci. 19(1), 126–141 (2021)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1 (2017)
Li, X., Metsis, V., Wang, H., Ngu, A.H.H.: TTS-GAN: a transformer-based time-series generative adversarial network. In: 20th International Conference on Artificial Intelligence in Medicine, pp. 133–143 (2022)
Yoon, J., Jarrett, D., Schaar, M.V.: Time-series generative adversarial networks. Adv. Neural Inf. Process. Syst. 32 (2019)
https://www.kaggle.com/competitions/pkdd-15-predict-taxi-service-trajectory-i/data
Wu, H., Chen, Z., Sun, W., Zheng, B., Wang, W.: Modeling trajectories with recurrent neural networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, vol. 25, pp. 3083–3090 (2017)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
Huang, D., et al.: A variational autoencoder based generative model of urban human mobility. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval, pp. 425–430. IEEE (2019)
Kulkarni, V., Garbinato, B.: Generating synthetic mobility traffic using RNNs. In: Proceed-ings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, pp. 1–4 (2017)
Liu, X., Chen, H., Andris, C.: trajGANs: using generative adversarial networks for geo-privacy protection of trajectory data (Vision paper). In: Location Privacy and Security Workshop, pp. 1–7 (2018)
Rao, J., Gao, S., Kang, Y., Huang, Q.: LSTM-TrajGAN: A deep learning approach to trajec-tory privacy protection. arXiv preprint arXiv:2006.10521 (2020)
Rossi, L., Paolanti, M., Pierdicca, R., Frontoni, E.: Human trajectory prediction and generation using LSTM models and GANs. Pattern Recogn. 120, 108136 (2021)
Choi, S., Kim, J., Yeo, H.: TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning. Transp. Res. Part C Emerg. Technol. 128, 103091 (2021)
Kim, J.W., Jang, B.: Deep learning-based privacy-preserving framework for synthetic tra-jectory generation. J. Netw. Comput. Appl. 206, 103459 (2022)
Esteban, C., Hyland, S.L., Rätsch, G.: Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:1706.02633 (2017)
Ramponi, G., Protopapas, P., Brambilla, M., Janssen, R.: T-CGAN: Conditional generative adversarial network for data augmentation in noisy time series with irregular sam-pling. arXiv preprint arXiv:1811.08295 (2018)
Gursoy, M.E., Liu, L., Truex, S., Yu, L., Wei, W.: Utility-aware synthesis of differentially private and attack-resilient location traces. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 196–211. ACM (2018)
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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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