Abstract
The pervasiveness of location-acquisition technologies leads to large amounts of spatio-temporal data, which brings researchers opportunities to discover interesting group patterns like congregation. Typically, a congregation is formed by a certain number of individuals within an area during a period of time. Previous work focused on discovering various congregations based on real-life scenarios to help in monitoring unusual group activities. However, most existing research didn’t further analyze these results due to the consideration that the congregation is an unusual event already. In this article, firstly, we propose a group pattern to capture a variety of congregations from trajectory data. Secondly, congregations are separated into unexpected congregations and periodic congregations by extracting spatio-temporal features from historical trajectories. Thirdly, we further investigate the intensity of periodic congregation and combine environmental factors to dynamically identify anomalies within it, together with previously obtained unexpected congregations to form abnormal congregations. Moreover, incremental update techniques are utilized to detect abnormal congregations over massive-scale trajectory streams online, which means it can immediately respond to the updated trajectories. Finally, based on real cellular network dataset and real taxi trajectory dataset, our approach is evaluated through extensive experiments which demonstrate its effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anbaroglu, B., Heydecker, B., Cheng, T.: Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks. Transp. Res. Part C: Emerg. Technol. 48, 47–65 (2014)
Anwar, T., Liu, C., Vu, H.L., Islam, M.S., Sellis, T.: Capturing the spatiotemporal evolution in road traffic networks. IEEE Trans. Knowl. Data Eng. 30(8), 1426–1439 (2018)
Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Comput. Geom. 41(3), 111–125 (2008)
Chen, T., Zhang, Y., Tuo, Y., Wang, W.: Online discovery of congregate groups on sparse spatio-temporal data. In: PIMRC, pp. 1–7 (2018)
Deng, D., Shahabi, C., Demiryurek, U., Zhu, L.: Situation aware multi-task learning for traffic prediction. In: ICDM, pp. 81–90 (2017)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996, pp. 226–231 (1996)
He, Y., Tan, H., Luo, W., Feng, S., Fan, J.: MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data. Front. Comput. Sci. 8(1), 83–99 (2014). https://doi.org/10.1007/s11704-013-3158-3
Jeung, H., Shen, H.T., Zhou, X.: Convoy queries in spatio-temporal databases. In: ICDE, pp. 1457–1459 (2008)
Kong, X., Song, X., Xia, F., Guo, H., Wang, J., Tolba, A.: LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web 21(3), 825–847 (2018). https://doi.org/10.1007/s11280-017-0487-4
Lan, R., Yu, Y., Cao, L., Song, P., Wang, Y.: Discovering evolving moving object groups from massive-scale trajectory streams. In: MDM, pp. 256–265 (2017)
Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. PVLDB 3(1), 723–734 (2010)
Lian, J., Zhang, L.: One-month Beijing taxi GPS trajectory dataset with taxi ids and vehicle status. In: DATA@SenSys, pp. 3–4 (2018)
Liu, S., Wang, S.: Trajectory community discovery and recommendation by multi-source diffusion modeling. IEEE Trans. Knowl. Data Eng. 29(4), 898–911 (2017)
Mondal, M.A., Rehena, Z.: Intelligent traffic congestion classification system using artificial neural network. In: WWW, pp. 110–116. ACM (2019)
Sun, F., Dubey, A., White, J.: DxNAT - deep neural networks for explaining non-recurring traffic congestion. In: IEEE BigData, pp. 2141–2150 (2017)
Wang, J., Gu, Q., Wu, J., Liu, G., Xiong, Z.: Traffic speed prediction and congestion source exploration: a deep learning method. In: ICDM, pp. 499–508 (2016)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: ACM SIGKDD, pp. 316–324 (2011)
Yuan, J., et al.: T-drive: driving directions based on taxi trajectories. In: ACM-GIS, pp. 99–108 (2010)
Zhan, X., Zheng, Y., Yi, X., Ukkusuri, S.V.: Citywide traffic volume estimation using trajectory data. IEEE Trans. Knowl. Data Eng. 29(2), 272–285 (2017)
Zheng, K., Zheng, Y., Yuan, N.J., Shang, S.: On discovery of gathering patterns from trajectories. In: ICDE, pp. 242–253 (2013)
Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant No. U1736218 and the Beijing Municipal Science & Technology Commission under Grant No. Z191100007119005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, T., Zhang, Y., Tuo, Y., Wang, W. (2020). Detecting Abnormal Congregation Through the Analysis of Massive Spatio-Temporal Data. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-60290-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60289-5
Online ISBN: 978-3-030-60290-1
eBook Packages: Computer ScienceComputer Science (R0)