Abstract
As motor vehicles are increasing, the demand for gas stations is rising. Because of the rising profits of gas stations, many traders have built illegal gas stations. The dangers of illegal gas stations are enormous. The government has always used traditional manual methods for screening illegal gas stations. How to quickly and effectively mine illegal gas stations in the trajectory data becomes a problem. This paper proposes an illegal gas station clustering discovery algorithm for unmarked trajectory data. The algorithm mines the suspected fueling point set and frequent staying point set of a single vehicle. Through the difference between the two, the suspected points of the illegal gas stations in the single vehicle trajectory are obtained, and finally all the illegal gas station suspicious points of the same type of vehicles are clustered to find the illegal gas station.
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
References
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6(3), 1–41 (2015)
Pan, G., Qi, G., Wu, Z., et al.: Land-Use classification using taxi GPS traces. IEEE Trans. Intell. Transp. Syst. 14(1), 113–123 (2013)
Calabrese, F., Colonna, M., Lovisolo, P., et al.: Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011)
Guo, D.: Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Vis. Comput. Graph. 15(6), 1041–1048 (2009)
Liu, H., Kan, Z., Wu, H., et al.: Vehicles’ refueling activity modeling and space-time distribution analysis. Bull. Surv. Mapp. 2016(9), 29–34
Niu, H., Liu, J., Fu, Y., Liu, Y., Lang, B.: Exploiting human mobility patterns for gas station site selection. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 242–257. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_16
Ester, M., Kriegel, H.P., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery & Data Mining (1996)
Phan, N., Poncelet, P., Teisseire, M.: All in one: mining multiple movement patterns. Int. J. Inf. Technol. Decis. Making 15, 1115–1156 (2016)
Chen, W., Oliverio, J., Kim, J.H., et al.: The modeling and simulation of data clustering algorithms in data mining with big data. J. Ind. Integr. Manag. (2018)
Hirano, S., Tsumoto, S.: Multiscale comparison and clustering of three-dimensional trajectories based on curvature maxima. Int. J. Inf. Technol. Decis. Making 09(06), 889–904 (2010)
Yang, Q., Wu, X.: 10 challenging problems in data mining research. Int. J. Inf. Technol. Decis. Making 05(04), 597–604 (2006)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lu, S., Li, G. (2020). Algorithms Research of the Illegal Gas Station Discovery Based on Vehicle Trajectory Data. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-2810-1_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2809-5
Online ISBN: 978-981-15-2810-1
eBook Packages: Computer ScienceComputer Science (R0)