Abstract
Trajectory prediction is a key task in the study of human mobility. This task can be done by considering a sequence of GPS locations and using different mechanisms to predict the following point that will be visited. The trajectory prediction is usually performed using methods like Markov Chains or architectures that rely on Recurrent Neural Networks (RNN). However, the use of Transformers neural networks has lately been adopted for sequential prediction tasks because of the increased efficiency achieved in training. In this paper, we propose AP-Traj (Attention and Possible directions for TRAJectory), which predicts a user’s next location based on the self-attention mechanism of the transformers encoding and a directed graph representing the road segments of the area visited. Our method achieves results comparable to the state-of-the-art model for this task but is up to 10 times faster.
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References
Aslak, U., Alessandretti, L.: Infostop: Scalable stop-location detection in multi-user mobility data. arXiv preprint arXiv:2003.14370 (2020)
Bray, J., Feldblum, J.T., Gilby, I.C.: Social bonds predict dominance trajectories in adult male chimpanzees. Anim. Behav. 179, 339–354 (2021)
Chen, Y.C., Thaipisutikul, T., Shih, T.K.: A learning-based poi recommendation with spatiotemporal context awareness. IEEE Trans. Cybern. 52(4), 2453–2466 (2022). https://doi.org/10.1109/TCYB.2020.3000733
Feng, J., Li, Y., Yang, Z., Qiu, Q., Jin, D.: Predicting human mobility with semantic motivation via multi-task attentional recurrent networks. IEEE Transactions on Knowledge and Data Engineering (2020)
Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Next place prediction using mobility Markov chains. In: Proceedings of the First Workshop on Measurement, Privacy, and Mobility, pp. 1–6 (2012)
Grigsby, J., Wang, Z., Qi, Y.: Long-range transformers for dynamic spatiotemporal forecasting. arXiv preprint arXiv:2109.12218 (2021)
Hong, Y., Martin, H., Raubal, M.: How do you go where? improving next location prediction by learning travel mode information using transformers. arXiv preprint arXiv:2210.04095 (2022)
Huang, L., Zhuang, J., Cheng, X., Xu, R., Ma, H.: STI-GAN: multimodal pedestrian trajectory prediction using spatiotemporal interactions and a generative adversarial network. IEEE Access 9, 50846–50856 (2021)
Liang, Y., Zhao, Z.: NetTraj: a network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms. IEEE Transactions on Intelligent Transportation Systems (2021)
Liu, R.W., et al.: STMGCN: mobile edge computing-empowered vessel trajectory prediction using spatio-temporal multi-graph convolutional network. IEEE Transactions on Industrial Informatics (2022)
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 352–361 (2009)
Pang, Y., Zhao, X., Hu, J., Yan, H., Liu, Y.: Bayesian spatio-temporal graph transformer network (b-star) for multi-aircraft trajectory prediction. Knowledge-Based Systems, p. 108998 (2022)
Shao, K., Wang, Y., Zhou, Z., Xie, X., Wang, G.: TrajForesee: how limited detailed trajectories enhance large-scale sparse information to predict vehicle trajectories? In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2189–2194. IEEE (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Yan, B., Zhao, G., Song, L., Yu, Y., Dong, J.: PreCLN: pretrained-based contrastive learning network for vehicle trajectory prediction. World Wide Web, pp. 1–23 (2022)
Yang, C., Gidofalvi, G.: Fast map matching, an algorithm integrating hidden Markov model with precomputation. Int. J. Geogr. Inf. Sci. 32(3), 547–570 (2018). https://doi.org/10.1080/13658816.2017.1400548
Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 316–324 (2011)
Yuan, J., et al. : T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108 (2010)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312–321 (2008)
Zheng, Y., Xie, X., Ma, W.Y., et al.: GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800 (2009)
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Galarreta, AP., Alatrista-Salas, H., Nunez-del-Prado, M. (2023). Predicting Next Whereabouts Using Deep Learning. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2023. Lecture Notes in Computer Science(), vol 13890. Springer, Cham. https://doi.org/10.1007/978-3-031-33498-6_15
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DOI: https://doi.org/10.1007/978-3-031-33498-6_15
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