skip to main content
10.1145/3615890.3628527acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery

Published: 11 December 2023 Publication History

Abstract

Trajectory on the road traffic is commonly collected at a low sampling rate, and trajectory recovery aims to recover a complete and continuous trajectory from the sparse and discrete inputs. Recently, sequential language models have been innovatively adopted for trajectory recovery in a pre-trained manner: it learns road segment representation vectors, which will be used in the downstream tasks. However, existing methods are incapable of handling complex trajectories: when the trajectory crosses remote road segments or makes several turns, which we call critical nodes, the quality of learned representations deteriorates, and the recovered trajectories skip the critical nodes. This work is dedicated to offering a more robust trajectory recovery for complex trajectories. Firstly, we define the trajectory complexity based on the detour score and entropy score, and construct the complexity-aware semantic graphs correspondingly. Then, we propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training from two aspects: 1) adaptively aggregate the multi-view graph features considering trajectory pattern, and 2) higher attention to critical nodes in a complex trajectory. Such that, our MGCAT is perceptual when handling the critical scenario of complex trajectories. Extensive experiments are conducted on large-scale datasets. The results prove that our method learns better representations for trajectory recovery, with 5.22% higher F1-score overall and 8.16% higher F1-score for complex trajectories particularly. The code is available here.

References

[1]
Marc Barthélemy. 2011. Spatial networks. Physics Reports 499, 1--3 (2011), 1--101.
[2]
Xinyu Chen, Jiajie Xu, Rui Zhou, Wei Chen, Junhua Fang, and Chengfei Liu. 2021. TrajVAE: A Variational AutoEncoder model for trajectory generation. Neurocomputing 428 (2021), 332--339.
[3]
Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long, Yiding Liu, Arun Kumar Chandran, and Richard Ellison. 2021. Robust Road Network Representation Learning: When Traffic Patterns Meet Traveling Semantics. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 211--220.
[4]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[5]
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. Advances in Neural Information Processing Systems 32 (2019).
[6]
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. 2021. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9588--9597.
[7]
Xiaomin Fang, Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, and Haifeng Wang. 2020. Constgat: Contextual spatial-temporal graph attention network for travel time estimation at baidu maps. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2697--2705.
[8]
Tao-Yang Fu and Wang-Chien Lee. 2020. TremBR: Exploring road networks for trajectory representation learning. ACM Transactions on Intelligent Systems and Technology (TIST) 11, 1 (2020), 1--25.
[9]
Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. 2019. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 3656--3663.
[10]
S. Hochreiter and J. Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735--1780.
[11]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[12]
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7871--7880.
[13]
Can Li, Lei Bai, Wei Liu, Lina Yao, and S Travis Waller. 2021. Urban mobility analytics: A deep spatial-temporal product neural network for traveler attributes inference. Transportation Research Part C: Emerging Technologies 124 (2021), 102921.
[14]
Ziyue Li. 2021. Tensor Topic Models with Graphs and Applications on Individualized Travel Patterns. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2756--2761.
[15]
Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, and Fugee Tsung. 2020. Tensor completion for weakly-dependent data on graph for metro passenger flow prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 4804--4810.
[16]
Zhishuai Li, Gang Xiong, Zebing Wei, Yisheng Lv, Noreen Anwar, and Fei-Yue Wang. 2022. A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices. IEEE Internet of Things Journal 9, 10 (2022), 7842--7852.
[17]
Yan Lin, Huaiyu Wan, Shengnan Guo, and Youfang Lin. 2021. 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. 4241--4248.
[18]
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).
[19]
Yin Lou, Chengyang Zhang, Yu Zheng, Xing Xie, Wei Wang, and Yan Huang. 2009. Map-matching for low-sampling-rate GPS trajectories. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. 352--361.
[20]
T. Mikolov, G. Corrado, C. Kai, and J. Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In Proceedings of the International Conference on Learning Representations (ICLR 2013).
[21]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119.
[22]
Paul Newson and John Krumm. 2009. Hidden Markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 336--343.
[23]
Huimin Ren, Sijie Ruan, Yanhua Li, Jie Bao, Chuishi Meng, Ruiyuan Li, and Yu Zheng. 2021. MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1410--1419.
[24]
Han Su, Shuncheng Liu, Bolong Zheng, Xiaofang Zhou, and Kai Zheng. 2020. A survey of trajectory distance measures and performance evaluation. VLDB Journal International Journal on Very Large Data Bases 29, 1 (2020), 3--32.
[25]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998--6008.
[26]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[27]
Jingyuan Wang, Ning Wu, Xinxi Lu, Xin Zhao, and Kai Feng. 2019. Deep trajectory recovery with fine-grained calibration using kalman filter. IEEE Transactions on Knowledge and Data Engineering (2019).
[28]
Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, and Wei Wang. 2017. Modeling trajectories with recurrent neural networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3083--3090.
[29]
Hao Wu, Jiangyun Mao, Weiwei Sun, Baihua Zheng, Hanyuan Zhang, Ziyang Chen, and Wei Wang. 2016. Probabilistic robust route recovery with spatio-temporal dynamics. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1915--1924.
[30]
Ning Wu, Xin Wayne Zhao, Jingyuan Wang, and Dayan Pan. 2020. Learning effective road network representation with hierarchical graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 6--14.
[31]
Feng Xie and David Levinson. 2007. Measuring the structure of road networks. Geographical analysis 39, 3 (2007), 336--356.
[32]
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020).
[33]
Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin. 2017. Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data. In Proceedings of the 26th International Conference on World Wide Web. 1241--1250.
[34]
Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, and Bin Yang. 2021. Unsupervised path representation learning with curriculum negative sampling. arXiv preprint arXiv:2106.09373 (2021).
[35]
Yuan Yao, Lorenzo Rosasco, and Andrea Caponnetto. 2007. On early stopping in gradient descent learning. Constructive Approximation 26, 2 (2007), 289--315.
[36]
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do Transformers Really Perform Bad for Graph Representation? arXiv preprint arXiv:2106.05234 (2021).
[37]
Pu Zhang, Lei Bai, Jianru Xue, Jianwu Fang, Nanning Zheng, and Wanli Ouyang. 2022. Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding. arXiv preprint arXiv:2202.01478 (2022).
[38]
Yu Zheng. 2015. Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology (TIST) 6, 3 (2015), 1--41.
[39]
Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2018. Trajectory-User Linking via Variational AutoEncoder. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3212--3218.
[40]
Fan Zhou, Hantao Wu, Goce Trajcevski, Ashfaq Khokhar, and Kunpeng Zhang. 2020. Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation. ACM Transactions on Spatial Algorithms and Systems (TSAS) 6, 2 (2020), 1--25.
[41]
Yang Zhou and Yan Huang. 2018. Deepmove: Learning place representations through large scale movement data. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2403--2412.
[42]
Yang Zhou, Yangxin Lin, Soyoung Ahn, Ping Wang, and Xin Wang. 2022. Platoon Trajectory Completion in a Mixed Traffic Environment Under Sparse Observation. IEEE Transactions on Intelligent Transportation Systems 23, 9 (2022), 16217--16226.

Cited By

View all
  • (2024)TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera RecordsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671558(5979-5990)Online publication date: 25-Aug-2024

Index Terms

  1. A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      GeoSearch '23: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data
      November 2023
      50 pages
      ISBN:9798400703522
      DOI:10.1145/3615890
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 11 December 2023

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. trajectory recovery
      2. pre-trained model
      3. graph
      4. transformer

      Qualifiers

      • Research-article

      Conference

      GeoSearch '23
      Sponsor:

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)80
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 14 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera RecordsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671558(5979-5990)Online publication date: 25-Aug-2024

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media