research-article

Error-Bounded Online Trajectory Simplification with Multi-Agent Reinforcement Learning

Authors:

Qianru ZhangAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 1758 - 1768

https://doi.org/10.1145/3447548.3467351

Published: 14 August 2021 Publication History

Abstract

Trajectory data has been widely used in various applications, including taxi services, traffic management, mobility analysis, etc. It is usually collected at a sensor's side in real time and corresponds to a sequence of sampled points. Constrained by the storage and/or network bandwidth of a sensor, it is common to simplify raw trajectory data when it is collected by dropping some sampled points. Many algorithms have been proposed for the error-bounded online trajectory simplification (EB-OTS) problem, which is to drop as many points as possible subject to that the error is bounded by an error tolerance. Nevertheless, these existing algorithms rely on pre-defined rules for decision making during the trajectory simplification process and there is no theoretical ground supporting their effectiveness. In this paper, we propose a multi-agent reinforcement learning method called MARL4TS for EB-OTS. MARL4TS involves two agents for different decision making problems during the trajectory simplification processes. Besides, MARL4TS has its objective equivalent to that of the EB-OTS problem, which provides some theoretical ground of its effectiveness. We conduct extensive experiments on real-world trajectory datasets, which verify that MARL4TS outperforms all existing algorithms in effectiveness and provides competitive efficiency.

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References

[1]

Richard Bellman. 1961. On the approximation of curves by line segments using dynamic programming. Commun. ACM, Vol. 4, 6 (1961), 284.

Digital Library

[2]

Yoshua Bengio. 2012. Practical recommendations for gradient-based training of deep architectures. In Neural networks: Tricks of the trade. Springer, 437--478.

Digital Library

[3]

Weiquan Cao and Yunzhao Li. 2017. DOTS: An online and near-optimal trajectory simplification algorithm. Journal of Systems and Software, Vol. 126 (2017), 34--44.

[4]

Chao Chen, Yan Ding, Xuefeng Xie, Shu Zhang, Zhu Wang, and Liang Feng. 2019. TrajCompressor: An online map-matching-based trajectory compression framework leveraging vehicle heading direction and change. IEEE TITS, Vol. 21, 5 (2019), 2012--2028.

[5]

Yunheng Han, Weiwei Sun, and Baihua Zheng. 2017. COMPRESS: A comprehensive framework of trajectory compression in road networks. TODS, Vol. 42, 2 (2017), 1--49.

Digital Library

[6]

John Edward Hershberger and Jack Snoeyink. 1992. Speeding up the Douglas-Peucker line-simplification algorithm. University of British Columbia, Department of Computer Science.

[7]

Bingqing Ke, Jie Shao, and Dongxiang Zhang. 2017. An efficient online approach for direction-preserving trajectory simplification with interval bounds. In MDM. IEEE, 50--55.

[8]

Bingqing Ke, Jie Shao, Yi Zhang, Dongxiang Zhang, and Yang Yang. 2016. An online approach for direction-based trajectory compression with error bound guarantee. In APWeb. Springer, 79--91.

[9]

Eamonn Keogh, Selina Chu, David Hart, and Michael Pazzani. 2001. An online algorithm for segmenting time series. In ICDM. IEEE, 289--296.

[10]

Jens Kober, J Andrew Bagnell, and Jan Peters. 2013. Reinforcement learning in robotics: A survey. IJRR, Vol. 32, 11 (2013), 1238--1274.

Digital Library

[11]

Ralph Lange, Frank Dürr, and Kurt Rothermel. 2011. Efficient real-time trajectory tracking. VLDBJ, Vol. 20, 5 (2011), 671--694.

Digital Library

[12]

Hyun-Rok Lee and Taesik Lee. 2019. Improved cooperative multi-agent reinforcement learning algorithm augmented by mixing demonstrations from centralized policy. In AAMAS. 1089--1098.

[13]

Tianyi Li, Ruikai Huang, Lu Chen, Christian S Jensen, and Torben Bach Pedersen. 2020 a. Compression of uncertain trajectories in road networks. PVLDB, Vol. 13, 7 (2020), 1050--1063.

Digital Library

[14]

Yexin Li, Yu Zheng, and Qiang Yang. 2019. Efficient and Effective Express via Contextual Cooperative Reinforcement Learning. In SIGKDD. 510--519.

[15]

Yexin Li, Yu Zheng, and Qiang Yang. 2020 b. Cooperative Multi-Agent Reinforcement Learning in Express System. In CIKM. 805--814.

[16]

Kaixiang Lin, Renyu Zhao, Zhe Xu, and Jiayu Zhou. 2018. Efficient large-scale fleet management via multi-agent deep reinforcement learning. In SIGKDD. 1774--1783.

[17]

Xuelian Lin, Jiahao Jiang, Shuai Ma, Yimeng Zuo, and Chunming Hu. 2019. One-pass trajectory simplification using the synchronous Euclidean distance. VLDBJ, Vol. 28, 6 (2019), 897--921.

[18]

Xuelian Lin, Shuai Ma, Han Zhang, Tianyu Wo, and Jinpeng Huai. 2017. One-pass error bounded trajectory simplification. PVLDB, Vol. 10, 7 (2017), 841--852.

Digital Library

[19]

Jiajun Liu, Kun Zhao, Philipp Sommer, Shuo Shang, Brano Kusy, and Raja Jurdak. 2015. Bounded quadrant system: Error-bounded trajectory compression on the go. In ICDE. IEEE, 987--998.

[20]

Jiajun Liu, Kun Zhao, Philipp Sommer, Shuo Shang, Brano Kusy, Jae-Gil Lee, and Raja Jurdak. 2016. A novel framework for online amnesic trajectory compression in resource-constrained environments. IEEE TKDE, Vol. 28, 11 (2016), 2827--2841.

[21]

Cheng Long, Raymond Chi-Wing Wong, and HV Jagadish. 2013. Direction-preserving trajectory simplification. PVLDB, Vol. 6, 10 (2013), 949--960.

Digital Library

[22]

Cheng Long, Raymond Chi-Wing Wong, and HV Jagadish. 2014. Trajectory simplification: on minimizing the direction-based error. PVLDB, Vol. 8, 1 (2014), 49--60.

Digital Library

[23]

Nirvana Meratnia and A Rolf. 2004. Spatiotemporal compression techniques for moving point objects. In EDBT. Springer, 765--782.

[24]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013).

[25]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et almbox. 2015. Human-level control through deep reinforcement learning. nature, Vol. 518, 7540 (2015), 529--533.

[26]

Jonathan Muckell, Jeong-Hyon Hwang, Vikram Patil, Catherine T Lawson, Fan Ping, and SS Ravi. 2011. SQUISH: an online approach for GPS trajectory compression. In Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications. ACM, 13.

Digital Library

[27]

Jonathan Muckell, Paul W Olsen, Jeong-Hyon Hwang, Catherine T Lawson, and SS Ravi. 2014. Compression of trajectory data: a comprehensive evaluation and new approach. GeoInformatica, Vol. 18, 3 (2014), 435--460.

Digital Library

[28]

Michalis Potamias, Kostas Patroumpas, and Timos Sellis. 2006. Sampling trajectory streams with spatiotemporal criteria. In SSDBM. IEEE, 275--284.

[29]

Martin L Puterman. 2014. Markov Decision Processes.: Discrete Stochastic Dynamic Programming. John Wiley & Sons.

[30]

Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press.

Digital Library

[31]

Goce Trajcevski, Hu Cao, Peter Scheuermanny, Ouri Wolfsonz, and Dennis Vaccaro. 2006. On-line data reduction and the quality of history in moving objects databases. In Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access. ACM, 19--26.

Digital Library

[32]

Zheng Wang, Cheng Long, and Gao Cong. 2021. Trajectory Simplification with Reinforcement Learning. In ICDE.

[33]

Zheng Wang, Cheng Long, Gao Cong, and Ce Ju. 2019. Effective and efficient sports play retrieval with deep representation learning. In SIGKDD. 499--509.

[34]

Zheng Wang, Cheng Long, Gao Cong, and Yiding Liu. 2020. Efficient and effective similar subtrajectory search with deep reinforcement learning. PVLDB, Vol. 13, 12 (2020), 2312--2325.

Digital Library

[35]

Dongxiang Zhang, Mengting Ding, Dingyu Yang, Yi Liu, Ju Fan, and Heng Tao Shen. 2018. Trajectory simplification: an experimental study and quality analysis. PVLDB, Vol. 11, 9 (2018), 934--946.

Digital Library

[36]

Yu Zheng, Xing Xie, Wei-Ying Ma, et al. 2010. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., Vol. 33, 2 (2010), 32--39.

Cited By

Gao YFang ZXu JGong SShen CChen L(2024)An Efficient and Distributed Framework for Real-Time Trajectory Stream ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331231936:5(1857-1873)Online publication date: May-2024
https://doi.org/10.1109/TKDE.2023.3312319
Chen XYu QDai SSun PTang HCheng L(2024)Deep Reinforcement Learning for Efficient IoT Data Compression in Smart Railroad ManagementIEEE Internet of Things Journal10.1109/JIOT.2023.334848711:15(25494-25504)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JIOT.2023.3348487
Wang ZLong CCong GJensen C(2024)Collectively Simplifying Trajectories in a Database: A Query Accuracy Driven Approach2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00334(4383-4395)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00334
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Index Terms

Error-Bounded Online Trajectory Simplification with Multi-Agent Reinforcement Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Cited By

Gao YFang ZXu JGong SShen CChen L(2024)An Efficient and Distributed Framework for Real-Time Trajectory Stream ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331231936:5(1857-1873)Online publication date: May-2024
https://doi.org/10.1109/TKDE.2023.3312319
Chen XYu QDai SSun PTang HCheng L(2024)Deep Reinforcement Learning for Efficient IoT Data Compression in Smart Railroad ManagementIEEE Internet of Things Journal10.1109/JIOT.2023.334848711:15(25494-25504)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JIOT.2023.3348487
Wang ZLong CCong GJensen C(2024)Collectively Simplifying Trajectories in a Database: A Query Accuracy Driven Approach2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00334(4383-4395)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00334
Ning ZXie L(2024)A survey on multi-agent reinforcement learning and its applicationJournal of Automation and Intelligence10.1016/j.jai.2024.02.0033:2(73-91)Online publication date: Jun-2024
https://doi.org/10.1016/j.jai.2024.02.003
Wu YFang JChen WZhao PZhao L(2024)Safety: A spatial and feature mixed outlier detection method for big trajectory dataInformation Processing & Management10.1016/j.ipm.2024.10367961:3(103679)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103679
Reyes GEstrada VTolozano-Benites RMaquilón V(2023)Batch Simplification Algorithm for Trajectories over Road NetworksISPRS International Journal of Geo-Information10.3390/ijgi1210039912:10(399)Online publication date: 30-Sep-2023
https://doi.org/10.3390/ijgi12100399
li wzhou l(2023)A trajectory simplification algorithm based on motion trend and variable speed characteristicsInternational Conference on Images, Signals, and Computing (ICISC 2023)10.1117/12.2691797(9)Online publication date: 21-Aug-2023
https://doi.org/10.1117/12.2691797
Fang ZHe CChen LHu DSun QLi LGao Y(2023)A Lightweight Framework for Fast Trajectory Simplification2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00184(2386-2399)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00184
Zhang QWang ZLong CHuang CYiu SLiu YCong GShi J(2023)Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00026(246-258)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00026

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