Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery (2404.19141v1)

Published 29 Apr 2024 in cs.LG

Abstract: Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in intelligent transportation systems. Although recent studies have demonstrated the advantages of achieving map-constrained trajectory recovery via an end-to-end manner, they still face two significant challenges. Firstly, existing methods are mostly sequence-based models. It is extremely hard for them to comprehensively capture the micro-semantics of individual trajectory, including the information of each GPS point and the movement between two GPS points. Secondly, existing approaches ignore the impact of the macro-semantics, i.e., the road conditions and the people's shared travel preferences reflected by a group of trajectories. To address the above challenges, we propose a Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder (MM-STGED). Specifically, we model each trajectory as a graph to efficiently describe the micro-semantics of trajectory and design a novel message-passing mechanism to learn trajectory representations. Additionally, we extract the macro-semantics of trajectories and further incorporate them into a well-designed graph-based decoder to guide trajectory recovery. Extensive experiments conducted on sparse trajectories with three different sampling intervals that are respectively constructed from two real-world trajectory datasets demonstrate the superiority of our proposed model.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. A. Derrow-Pinion, J. She, D. Wong, O. Lange, T. Hester, L. Perez, M. Nunkesser, S. Lee, X. Guo, B. Wiltshire et al., “Eta prediction with graph neural networks in google maps,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 3767–3776.
  2. Z. Chen, X. Xiao, Y.-J. Gong, J. Fang, N. Ma, H. Chai, and Z. Cao, “Interpreting trajectories from multiple views: a hierarchical self-attention network for estimating the time of arrival,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 2771–2779.
  3. Z. Yang, H. Sun, J. Huang, Z. Sun, H. Xiong, S. Qiao, Z. Guan, and X. Jia, “An efficient destination prediction approach based on future trajectory prediction and transition matrix optimization,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 2, pp. 203–217, 2018.
  4. H. Wen, Y. Lin, F. Wu, H. Wan, S. Guo, L. Wu, C. Song, and Y. Xu, “Package pick-up route prediction via modeling couriers’ spatial-temporal behaviors,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE).   IEEE, 2021, pp. 2141–2146.
  5. H. Wen, Y. Lin, X. Mao, F. Wu, Y. Zhao, H. Wang, J. Zheng, L. Wu, H. Hu, and H. Wan, “Graph2route: A dynamic spatial-temporal graph neural network for pick-up and delivery route prediction,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 4143–4152.
  6. Y. Liu, K. Zhao, G. Cong, and Z. Bao, “Online anomalous trajectory detection with deep generative sequence modeling,” in 2020 IEEE 36th International Conference on Data Engineering (ICDE).   IEEE, 2020, pp. 949–960.
  7. X. Han, R. Cheng, C. Ma, and T. Grubenmann, “Deeptea: Effective and efficient online time-dependent trajectory outlier detection,” Proceedings of the VLDB Endowment, vol. 15, no. 7, pp. 1493–1505, 2022.
  8. J. Yuan, Y. Zheng, C. Zhang, X. Xie, and G.-Z. Sun, “An interactive-voting based map matching algorithm,” in Proceedings of the 2010 Eleventh International Conference on Mobile Data Management, 2010, pp. 43–52.
  9. H. Ren, S. Ruan, Y. Li, J. Bao, C. Meng, R. Li, and Y. Zheng, “Mtrajrec: Map-constrained trajectory recovery via seq2seq multi-task learning,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 1410–1419.
  10. S. Hoteit, S. Secci, S. Sobolevsky, C. Ratti, and G. Pujolle, “Estimating human trajectories and hotspots through mobile phone data,” Computer Networks, vol. 64, pp. 296–307, 2014.
  11. J. Wang, N. Wu, X. Lu, W. X. Zhao, and K. Feng, “Deep trajectory recovery with fine-grained calibration using kalman filter,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 3, pp. 921–934, 2019.
  12. G. R. Jagadeesh and T. Srikanthan, “Online map-matching of noisy and sparse location data with hidden markov and route choice models,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2423–2434, 2017.
  13. K. Zhao, J. Feng, Z. Xu, T. Xia, L. Chen, F. Sun, D. Guo, D. Jin, and Y. Li, “Deepmm: Deep learning based map matching with data augmentation,” in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019, pp. 452–455.
  14. P. Newson and J. Krumm, “Hidden markov map matching through noise and sparseness,” in Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, 2009, pp. 336–343.
  15. Y. Chen, H. Zhang, W. Sun, and B. Zheng, “Rntrajrec: Road network enhanced trajectory recovery with spatial-temporal transformer,” in 2023 IEEE 39th International Conference on Data Engineering (ICDE).   IEEE, 2023, pp. 829–842.
  16. A. Sadri, F. D. Salim, Y. Ren, W. Shao, J. C. Krumm, and C. Mascolo, “What will you do for the rest of the day? an approach to continuous trajectory prediction,” Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, vol. 2, no. 4, pp. 1–26, 2018.
  17. Z. Fang, Y. Du, X. Zhu, D. Hu, L. Chen, Y. Gao, and C. S. Jensen, “Spatio-temporal trajectory similarity learning in road networks,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 347–356.
  18. H. Zhang, X. Zhang, Q. JIANG, B. Zheng, Z. Sun, and W. Sun, “Trajectory similarity learning with auxiliary supervision and optimal matching.(2020),” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan, 2020, pp. 11–17.
  19. G. Li, C.-C. Hung, M. Liu, L. Pan, W.-C. Peng, and S.-H. G. Chan, “Spatial-temporal similarity for trajectories with location noise and sporadic sampling,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE).   IEEE, 2021, pp. 1224–1235.
  20. D. Zhang, N. Li, Z.-H. Zhou, C. Chen, L. Sun, and S. Li, “ibat: detecting anomalous taxi trajectories from gps traces,” in Proceedings of the 13th international conference on Ubiquitous computing, 2011, pp. 99–108.
  21. M. M. Elshrif, K. Isufaj, and M. F. Mokbel, “Network-less trajectory imputation,” in Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022, pp. 1–10.
  22. C. Chen, S. Jiao, S. Zhang, W. Liu, L. Feng, and Y. Wang, “Tripimputor: Real-time imputing taxi trip purpose leveraging multi-sourced urban data,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 10, pp. 3292–3304, 2018.
  23. B. Li, Z. Cai, M. Kang, S. Su, S. Zhang, L. Jiang, and Y. Ge, “A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks,” International Journal of Geographical Information Science, vol. 35, no. 4, pp. 717–740, 2021.
  24. L.-Y. Wei, Y. Zheng, and W.-C. Peng, “Constructing popular routes from uncertain trajectories,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, pp. 195–203.
  25. Z. Chen, H. T. Shen, and X. Zhou, “Discovering popular routes from trajectories,” in 2011 IEEE 27th International Conference on Data Engineering.   IEEE, 2011, pp. 900–911.
  26. R. Song, W. Lu, W. Sun, Y. Huang, and C. Chen, “Quick map matching using multi-core cpus,” in Proceedings of the 20th International Conference on Advances in Geographic Information Systems, 2012, pp. 605–608.
  27. Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang, “Map-matching for low-sampling-rate gps trajectories,” in Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, 2009, pp. 352–361.
  28. E. Rappos, S. Robert, and P. Cudré-Mauroux, “A force-directed approach for offline gps trajectory map matching,” in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018, pp. 319–328.
  29. Y. Liu, Q. Ge, W. Luo, Q. Huang, L. Zou, H. Wang, X. Li, and C. Liu, “Graphmm: Graph-based vehicular map matching by leveraging trajectory and road correlations,” IEEE Transactions on Knowledge and Data Engineering, 2023.
  30. J. Si, J. Yang, Y. Xiang, H. Wang, L. Li, R. Zhang, B. Tu, and X. Chen, “Trajbert: Bert-based trajectory recovery with spatial-temporal refinement for implicit sparse trajectories,” IEEE Transactions on Mobile Computing, 2023.
  31. Z. Lin, G. Zhang, Z. He, J. Feng, W. Wu, and Y. Li, “Vehicle trajectory recovery on road network based on traffic camera video data,” in Proceedings of the 29th International Conference on Advances in Geographic Information Systems, 2021, pp. 389–398.
  32. F. Yu, W. Ao, H. Yan, G. Zhang, W. Wu, and Y. Li, “Spatio-temporal vehicle trajectory recovery on road network based on traffic camera video data,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 4413–4421.
  33. X. Li, K. Zhao, G. Cong, C. S. Jensen, and W. Wei, “Deep representation learning for trajectory similarity computation,” in 2018 IEEE 34th international conference on data engineering (ICDE).   IEEE, 2018, pp. 617–628.
  34. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  35. P. Yang, H. Wang, Y. Zhang, L. Qin, W. Zhang, and X. Lin, “T3s: Effective representation learning for trajectory similarity computation,” in 2021 IEEE 37th International Conference on Data Engineering (ICDE).   IEEE, 2021, pp. 2183–2188.
  36. D. Yao, G. Cong, C. Zhang, and J. Bi, “Computing trajectory similarity in linear time: A generic seed-guided neural metric learning approach,” in 2019 IEEE 35th international conference on data engineering (ICDE).   IEEE, 2019, pp. 1358–1369.
  37. D. Yao, C. Zhang, Z. Zhu, J. Huang, and J. Bi, “Trajectory clustering via deep representation learning,” in 2017 international joint conference on neural networks (IJCNN).   IEEE, 2017, pp. 3880–3887.
  38. T. Xia, Y. Qi, J. Feng, F. Xu, F. Sun, D. Guo, and Y. Li, “Attnmove: History enhanced trajectory recovery via attentional network,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, 2021, pp. 4494–4502.
  39. J. Jiang, D. Pan, H. Ren, X. Jiang, C. Li, and J. Wang, “Self-supervised trajectory representation learning with temporal regularities and travel semantics,” arXiv preprint arXiv:2211.09510, 2022.
  40. D. Yao, H. Hu, L. Du, G. Cong, S. Han, and J. Bi, “Trajgat: A graph-based long-term dependency modeling approach for trajectory similarity computation,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 2275–2285.
  41. H. Sun, C. Yang, L. Deng, F. Zhou, F. Huang, and K. Zheng, “Periodicmove: Shift-aware human mobility recovery with graph neural network,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 1734–1743.
  42. S. Zheng, Y. Yue, and J. Hobbs, “Generating long-term trajectories using deep hierarchical networks,” Advances in Neural Information Processing Systems, vol. 29, 2016.
  43. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  44. M. Welling and T. N. Kipf, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations, 2016.
  45. A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 855–864.
  46. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019.
Citations (5)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets