Abstract
The advancement of position acquisition technology has enabled the study based on vehicle trajectories. However, limitations in equipment and environmental factors often result in missing track records, significantly impacting the trajectory data quality. It is a fundamental task to restore the missing vehicle tracks within the traffic network structure. Existing research has attempted to address this issue through the construction of neural network models. However, these methods neglect the significance of the bidirectional information of the trajectory and the embedded representation of the trajectory unit. In view of the above problems, we propose a Seq2Seq-based trajectory recovery model that effectively utilizes bidirectional information and generates embedded representations of trajectory units to enhance trajectory recovery performance, which is a Pre-Training and Bidirectional Semantic enhanced Trajectory Recovery model, namely PBTR. Specifically, the road network’s representations extracting time factors are captured by a pre-training technique and a bidirectional semantics encoder is employed to enhance the expressiveness of the model followed by an attentive recurrent network to reconstruct the trajectory. The efficacy of our model is demonstrated through its superior performance on two real-world datasets.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder XGBoostfor statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Erhan, D., Courville, A., Bengio, Y., Vincent, P.: Why does unsupervised pre-training help deep learning? In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 201–208. JMLR Workshop and Conference Proceedings (2010)
Feng, J., et al.: DeepMove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 1459–1468 (2018)
Han, L., Du, B., Lin, J., Sun, L., Li, X., Peng, Y.: Multi-semantic path representation learning for travel time estimation. IEEE Trans. Intell. Transp. Syst. 23(8), 13108–13117 (2021)
Hendrycks, D., Lee, K., Mazeika, M.: Using pre-training can improve model robustness and uncertainty. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 2712–2721. PMLR (2019)
Li, X., Zhao, K., Cong, G., Jensen, C.S., Wei, W.: Deep representation learning for trajectory similarity computation. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 617–628. IEEE (2018)
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 Conference on Artificial Intelligence, vol. 35, pp. 4241–4248 (2021)
Liu, Y., Zhao, K., Cong, G., Bao, Z.: Online anomalous trajectory detection with deep generative sequence modeling. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 949–960. IEEE (2020)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Park, S.H., Kim, B., Kang, C.M., Chung, C.C., Choi, J.W.: Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1672–1678. IEEE (2018)
Ren, H., Ruan, S., Li, Y., Bao, J., Meng, C., Li, R., Zheng, Y.: MTrajRec: map-constrained trajectory recovery via seq2seq multi-task learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1410–1419 (2021)
Shimizu, T., Yabe, T., Tsubouchi, K.: Learning fine grained place embeddings with spatial hierarchy from human mobility trajectories. arXiv preprint arXiv:2002.02058 (2020)
Sun, H., Yang, C., Deng, L., Zhou, F., Huang, F., Zheng, K.: PeriodicMove: shift-aware human mobility recovery with graph neural network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1734–1743 (2021)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Wan, H., Li, F., Guo, S., Cao, Z., Lin, Y.: Learning time-aware distributed representations of locations from spatio-temporal trajectories. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 268–272. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18590-9_26
Wang, J., Wu, N., Lu, X., Zhao, W.X., Feng, K.: Deep trajectory recovery with fine-grained calibration using Kalman filter. IEEE Trans. Knowl. Data Eng. 33(3), 921–934 (2019)
Wu, R., Luo, G., Shao, J., Tian, L., Peng, C.: Location prediction on trajectory data: a review. Big Data Min. Anal. 1(2), 108–127 (2018)
Xia, T., et al.: AttnMove: history enhanced trajectory recovery via attentional network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4494–4502 (2021)
Xu, Y., Sun, L., Du, B., Han, L.: Spatial semantic learning for travel time estimation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds.) KSEM 2022, Part III. LNCS, vol. 13370, pp. 15–26. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10989-8_2
Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2011)
Zhao, S., Zhao, T., King, I., Lyu, M.R.: Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 153–162 (2017)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 62272023) and the Fundamental Research Funds for the Central Universities (No. YWF-23-L-1203).
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Zhang, Q., Liao, T., Zhu, T., Sun, L., Lv, W. (2024). PBTR: Pre-training and Bidirectional Semantic Enhanced Trajectory Recovery. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_1
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