Abstract
Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing applications. However, these trajectories, typically collected from location-based services, often suffer from sparsity and irregularity in time. To support the development of mobile applications, there is a need to recover or estimate missing locations of unobserved time slots in these trajectories at a fine-grained spatial-temporal resolution. Existing methods for trajectory recovery rely on either individual user trajectories or collective mobility patterns from all users. The potential to combine individual and collective patterns for precise trajectory recovery remains unexplored. Additionally, current methods are sensitive to the heterogeneous temporal distributions of the observable trajectory segments. In this paper, we propose CLMove (where CL stands for contrastive learning), a novel model designed to capture multilevel mobility patterns and enhance robustness in trajectory recovery. CLMove features a two-stage location encoder that captures collective and individual mobility patterns. The graph neural network based networks in CLMove explore location transition patterns within a single trajectory and across various user trajectories. We also design a trajectory-level contrastive learning task to improve the robustness of the model. Extensive experimental results on three representative real-world datasets demonstrate that our CLMove model consistently outperforms state-of-the-art methods in terms of trajectory recovery accuracy.
摘要
在城市计算应用中, 用户轨迹数据是用户移动行为分析的基础数据源. 然而, 由于这些用户轨迹数据部分是从基于位置的服务中收集的, 在时间上常常具有稀疏性和不规则性. 为提高基于位置数据服务的性能, 以较高时空分辨率对用户轨迹数据进行恢复, 对无记录时刻的用户地点进行预测是非常重要的. 本文提出一个新的轨迹恢复模型, 旨在捕捉多级移动模式并增强轨迹恢复的稳健性. 该模型具有一个两阶段位置编码器, 用于捕捉集体和个体移动模式, 并利用基于图神经网络的网络与注意力机制捕捉单个轨迹内部和跨多个用户轨迹的位置转移模式. 此外, 采用一个轨迹级对比学习任务以提高模型的稳健性. 在3个具有代表性的真实数据集上的大量实验结果表明, 该模型在轨迹恢复精度方面始终具有优越的性能.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Chandio AA, Tziritas N, Zhang F, et al., 2016. Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories. Front Inform Technol Electron Eng, 17(12): 1305–1319. https://doi.org/10.1631/FITEE.1600027
Chen GS, Viana AC, Fiore M, et al., 2019. Complete trajectory reconstruction from sparse mobile phone data. EPJ Data Sci, 8(1): 30. https://doi.org/10.1140/epjds/s13688-019-0206-8
Chen T, Kornblith S, Norouzi M, et al., 2020. A simple framework for contrastive learning of visual representations. Proc 37th Int Conf on Machine Learning, p.1597–1607.
Chen XL, He KM, 2021. Exploring simple Siamese representation learning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.15745–15753. https://doi.org/10.1109/CVPR46437.2021.01549
Cho E, Myers SA, Leskovec J, 2011. Friendship and mobility: user movement in location-based social networks. Proc 17th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1082–1090. https://doi.org/10.1145/2020408.2020579
Chondrogiannis T, Bornholdt J, Bouros P, et al., 2022. History oblivious route recovery on road networks. Proc 30th Int Conf on Advances in Geographic Information Systems, Article 44. https://doi.org/10.1145/3557915.3560979
Chung J, Gülçehre Ç, Cho K, et al., 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. https://arxiv.org/abs/1412.3555
Deng LW, Zhao Y, Fu ZD, et al., 2022. Efficient trajectory similarity computation with contrastive learning. Proc 31st ACM Int Conf on Information & Knowledge Management, p.365–374. https://doi.org/10.1145/3511808.3557308
Dhont M, Tsiporkova E, González-Deleito N, 2022. Mining of spatiotemporal trajectory profiles derived from mobility data. IEEE Int Conf on Data Mining Workshops, p.1–9. https://doi.org/10.1109/ICDMW58026.2022.00133
Fang ZH, Yang Y, Yang G, et al., 2021. CellSense: human mobility recovery via cellular network data enhancement. Proc ACM Interact Mob Wearab Ubiquit Technol, 5(3): 100. https://doi.org/10.1145/3478087
Fang ZQ, Du YT, Zhu XJ, et al., 2022. Spatio-temporal trajectory similarity learning in road networks. Proc 28th ACM SIGKDD Conf on Knowledge Discovery and Data Mining, p.347–356. https://doi.org/10.1145/3534678.3539375
Feng J, Li Y, Zhang C, et al., 2018. DeepMove: predicting human mobility with attentional recurrent networks. Proc World Wide Web Conf, p.1459–1468. https://doi.org/10.1145/3178876.3186058
Gao Q, Wang XH, Liu CR, et al., 2023. Open anomalous trajectory recognition via probabilistic metric learning. Proc 32nd Int Joint Conf on Artificial Intelligence, p.2095–2103. https://doi.org/10.24963/ijcai.2023/233
González MC, Hidalgo CA, Barabási AL, 2008. Understanding individual human mobility patterns. https://arxiv.org/abs/0806.1256
He KM, Fan HQ, Wu YX, et al., 2020. Momentum contrast for unsupervised visual representation learning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.9726–9735. https://doi.org/10.1109/CVPR42600.2020.00975
Hristova D, Williams MJ, Musolesi M, et al., 2016. Measuring urban social diversity using interconnected geo-social networks. Proc 25th Int Conf on World Wide Web, p.21–30. https://doi.org/10.1145/2872427.2883065
Li L, Li YB, Li ZH, 2013. Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transp Res Part C Emerg Technol, 34: 108–120. https://doi.org/10.1016/j.trc.2013.05.008
Li XC, Zhao KQ, Cong G, et al., 2018. Deep representation learning for trajectory similarity computation. Proc IEEE 34th Int Conf on Data Engineering, p.617–628. https://doi.org/10.1109/ICDE.2018.00062
Li XC, Cong G, Cheng Y, 2020. Spatial transition learning on road networks with deep probabilistic models. Proc IEEE 36th Int Conf on Data Engineering, p.349–360. https://doi.org/10.1109/ICDE48307.2020.00037
Li YJ, Tarlow D, Brockschmidt M, et al., 2016. Gated graph sequence neural networks. Proc 4th Int Conf on Learning Representations.
Lin Y, Wan HY, Guo SN, et al., 2021. Pre-training context and time aware location embeddings from spatial-temporal trajectories for user next location prediction. Proc 35th AAAI Conf on Artificial Intelligence, p.4241–4248. https://doi.org/10.1609/aaai.v35i5.16548
Liu Q, Wu S, Wang L, et al., 2016. Predicting the next location: a recurrent model with spatial and temporal contexts. Proc 30th AAAI Conf on Artificial Intelligence, p.194–200. https://doi.org/10.1609/aaai.v30i1.9971
Luo YH, Cai XR, Zhang Y, et al., 2018. Multivariate time series imputation with generative adversarial networks. Proc 32nd Int Conf on Neural Information Processing Systems, p.1603–1614.
Noulas A, Shaw B, Lambiotte R, et al., 2015. Topological properties and temporal dynamics of place networks in urban environments. Proc 24th Int Conf on World Wide Web, p.431–441. https://doi.org/10.1145/2740908.2745402
Park D, Kang J, Song H, et al., 2022. Multi-view POI-level cellular trajectory reconstruction for digital contact tracing of infectious diseases. Proc IEEE Int Conf on Data Mining, p.1137–1142. https://doi.org/10.1109/ICDM54844.2022.00144
Ren HM, Ruan SJ, Li YH, et al., 2021. MTrajRec: map-constrained trajectory recovery via Seq2Seq multi-task learning. Proc 27th ACM SIGKDD Conf on Knowledge Discovery & Data Mining, p.1410–1419. https://doi.org/10.1145/3447548.3467238
Salakhutdinov R, Mnih A, 2007. Probabilistic matrix factorization. Proc 20th Int Conf on Neural Information Processing Systems, p.1257–1264.
Seng D, Lv FS, Liang ZY, et al., 2021. Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit. Front Inform Technol Electron Eng, 22(9): 1179–1193. https://doi.org/10.1631/FITEE.2000243
Si JJ, Yang J, Xiang Y, et al., 2024. TrajBERT: BERT-based trajectory recovery with spatial-temporal refinement for implicit sparse trajectories. IEEE Trans Mob Comput, 23(5): 4849–4860. https://doi.org/10.1109/TMC.2023.3297115
Sun H, Yang CJ, Deng LW, et al., 2021. PeriodicMove: shift-aware human mobility recovery with graph neural network. Proc 30th ACM Int Conf on Information & Knowledge Management, p.1734–1743. https://doi.org/10.1145/3459637.3482284
Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Proc 31st Int Conf on Neural Information Processing Systems, p.6000–6010.
Wang YY, Jiang WH, Pu SL, et al., 2020. Learning embeddings of a heterogeneous behavior network for potential behavior prediction. Front Inform Technol Electron Eng, 21(3): 422–435. https://doi.org/10.1631/FITEE.1800493
Wei LY, Zheng Y, Peng WC, 2012. Constructing popular routes from uncertain trajectories. Proc 18th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.195–203. https://doi.org/10.1145/2339530.2339562
Wu H, Mao JY, Sun WW, et al., 2016. Probabilistic robust route recovery with spatio-temporal dynamics. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1915–1924. https://doi.org/10.1145/2939672.2939843
Wu S, Tang YY, Zhu YQ, et al., 2019. Session-based recommendation with graph neural networks. Proc 33rd AAAI Conf on Artificial Intelligence, p.346–353. https://doi.org/10.1609/aaai.v33i01.3301346
Xi DB, Zhuang FZ, Liu YC, et al., 2019. Modelling of bidirectional spatio-temporal dependence and users’ dynamic preferences for missing POI check-in identification. Proc 33rd AAAI Conf on Artificial Intelligence, p.5458–5465. https://doi.org/10.1609/aaai.v33i01.33015458
Xia T, Qi YH, Feng J, et al., 2021. AttnMove: history enhanced trajectory recovery via attentional network. Proc 35th AAAI Conf on Artificial Intelligence, p.4494–4502. https://doi.org/10.1609/aaai.v35i5.16577
Xu Y, Xu JJ, Zhao J, et al., 2022. MetaPTP: an adaptive meta-optimized model for personalized spatial trajectory prediction. Proc 28th ACM SIGKDD Conf on Knowledge Discovery and Data Mining, p.2151–2159. https://doi.org/10.1145/3534678.3539360
Yang DQ, Zhang DQ, Zheng VW, et al., 2015. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans Syst Man Cybern Syst, 45(1): 129–142. https://doi.org/10.1109/TSMC.2014.2327053
Yang S, Liu JM, Zhao KQ, 2022. GETNext: trajectory flow map enhanced Transformer for next POI recommendation. Proc 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1144–1153. https://doi.org/10.1145/3477495.3531983
Zhang WY, Xia DW, Chang GY, et al., 2022. APFD: an effective approach to taxi route recommendation with mobile trajectory big data. Front Inform Technol Electron Eng, 23(10): 1494–1510. https://doi.org/10.1631/FITEE.2100530
Zhao J, Xu JJ, Zhou R, et al., 2018. On prediction of user destination by sub-trajectory understanding: a deep learning based approach. Proc 27th ACM Int Conf on Information and Knowledge Management, p.1413–1422. https://doi.org/10.1145/3269206.3271708
Zheng Y, Li QN, Chen YK, et al., 2008. Understanding mobility based on GPS data. Proc 10th Int Conf on Ubiquitous Computing, p.312–321. https://doi.org/10.1145/1409635.1409677
Zheng Y, Zhang LZ, Xie X, et al., 2009. Mining interesting locations and travel sequences from GPS trajectories. Proc 18th Int Conf on World Wide Web, p.791–800. https://doi.org/10.1145/1526709.1526816
Zheng Y, Xie X, Ma WY, 2010. GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull, 33(2): 32–39.
Zhou F, Wang PY, Xu X, et al., 2022. Contrastive trajectory learning for tour recommendation. ACM Trans Intell Syst Technol, 13(1): 4. https://doi.org/10.1145/3462331
Author information
Authors and Affiliations
Contributions
Yushan LIU and Yang CHEN designed the research. Yushan LIU processed the data and drafted the paper. Yang CHEN and Jiayun ZHANG helped organize the paper. Yu XIAO and Xin WANG revised and finalized the paper.
Corresponding author
Ethics declarations
All the authors declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (Nos. 62072115 and 61971145) and the Shanghai Science and Technology Innovation Action Plan Project, China (No. 22510713600)
Rights and permissions
About this article
Cite this article
Liu, Y., Chen, Y., Zhang, J. et al. Toward an accurate mobility trajectory recovery using contrastive learning. Front Inform Technol Electron Eng 25, 1479–1496 (2024). https://doi.org/10.1631/FITEE.2300647
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2300647