Abstract
While the ever-increasing amount of available data has enabled complex machine learning algorithms in various application areas, maintaining data privacy has become more and more critical. This is especially true for mobility data. In nearly all cases, mobility data is personal and therefore the drivers’ privacy needs to be protected. However, mobility data is particularly hard to anonymize, hindering its use in machine learning algorithms to its full potential. In this paper, we address these challenges by generating synthetic vehicle trajectories that are not subject to personal data protection but have the same statistical characteristics as the originals. We present CondTraj-GAN– Conditional Trajectory Generative Adversarial Network. – a novel end-to-end framework to generate entirely synthetic vehicle trajectories. We introduce a specialized training and inference procedure that enables the application of GANs to discrete trajectory data conditioned on their sequence length. We demonstrate the data utility of the synthetic trajectories by comparing their spatial characteristics with the original dataset. Finally, our evaluation shows that CondTraj-GAN reliably outperforms state-of-the-art trajectory generation baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the ACM SIGSAC (2013)
Cao, C., Li, M.: Generating Mobility Trajectories with Retained Data Utility. In: Proceedings of the ACM SIGKDD (2021)
Chen, X., Xu, J., Zhou, R., Chen, W., Fang, J., Liu, C.: TrajVAE: a variational AutoEncoder model for trajectory generation. Neurocomputing 428, 332–339 (2021)
Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 900–911 (2011)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) Automata, Languages and Programming (2006)
Fu, H., Wang, Z., Yu, Y., Meng, X., Liu, G.: Traffic flow driven spatio-temporal graph convolutional network for ride-hailing demand forecasting. In: Advances in Knowledge Discovery and Data Mining (2021)
Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of NIPS (2014)
Huang, D., et al.: A variational autoencoder based generative model of urban human mobility. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 425–430 (2019)
Jiang, S., Yang, Y., Gupta, S., Veneziano, D., Athavale, S., González, M.C.: The TimeGeo modeling framework for urban mobility without travel surveys. In: Proceedings of the National Academy of Sciences (2016)
Li, M., Tong, P., Li, M., Jin, Z., Huang, J., Hua, X.S.: Traffic flow prediction with vehicle trajectories. In: Proceedings of the AAAI (2021)
Lin, Y., Wan, H., Guo, S., Lin, Y.: Pre-training context and time aware location embeddings from spatial-temporal trajectories for user next location prediction. In: Proceedings of the AAAI (2021)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR (2014)
Ouyang, K., Shokri, R., Rosenblum, D.S., Yang, W.: A non-parametric generative model for human trajectories. In: IJCAI International Joint Conference on Artificial Intelligence 2018-July, pp. 3812–3817 (2018)
Pappalardo, L., Simini, F.: Data-driven generation of spatio-temporal routines in human mobility. Data Min. Knowl. Disc. 32(3), 787–829 (2017). https://doi.org/10.1007/s10618-017-0548-4
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with CLIP latents. CoRR (2022)
Rao, J., Gao, S., Kang, Y., Huang, Q.: LSTM-TrajGAN: a deep learning approach to trajectory privacy protection. In: LIPIcs (2020)
Terrovitis, M., Mamoulis, N.: Privacy preservation in the publication of trajectories. In: Proceedings of the IEEE International Conference on Mobile Data Management (2008)
Villani, C.: Optimal transport: old and new, vol. 338. Springer (2009). https://doi.org/10.1007/978-3-540-71050-9
Wang, X., Liu, X., Lu, Z., Yang, H.: Large scale GPS trajectory generation using map based on two stage GAN. J. Data Sci. 19, 126–141 (2021)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Proceedings of the AAAI 2017 (2017)
Zhao, J., Mei, J., Matwin, S., Su, Y., Yang, Y.: Risk-aware individual trajectory data publishing with differential privacy. IEEE Access (2021)
Zhao, X., Spall, J.C.: A markovian framework for modeling dynamic network traffic. In: 2018 Annual American Control Conference (ACC) (2018)
Acknowledgements
This work was partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the project “CampaNeo” (grant ID 01MD 19007A).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Henke, N., Wonsak, S., Mitra, P., Nolting, M., Tempelmeier, N. (2023). CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13936. Springer, Cham. https://doi.org/10.1007/978-3-031-33377-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-33377-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33376-7
Online ISBN: 978-3-031-33377-4
eBook Packages: Computer ScienceComputer Science (R0)