Abstract
Traffic congestion has been an important problem all over the world. It is necessary to discover meaningful traffic patterns such as congestion propagation patterns from the massive historical dataset. Existed methods focusing on discovering congestion propagation patterns can’t mine transitivity of time and space very well. The spatio-temporal co-location pattern mining discovers the subsets of features which are located together in adjacent time periods frequently. So we propose using the spatio-temporal co-location pattern mining to discover congestion propagation patterns. Firstly, we propose the concepts of Spatio-Temporal Congestion Co-location Pattern (STCCP). Secondly, we give a framework and an algorithm for mining STCCPs. Finally, we validate our algorithm on real data sets. The results show that our method can effectively discover congestion propagation patterns.
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Nguyen, H., Liu, W., Chen, F.: Discovering congestion propagation patterns in spatio-temporal traffic data. IEEE Trans. Big Data 3(2), 169–180 (2017)
Liu W., Zheng Y., Chawla S., et al.: Discovering spatio-temporal causal interactions in traffic data streams. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1010–1018. Elsevier (2011)
Pang, L., Chawla, S., Liu, W., et al.: On detection of emerging anomalous traffic patterns using GPS data. Data Knowl. Eng. 87(9), 357–373 (2013)
Chu, V.W., Wong, R.K., Liu, W., Chen, F.: Causal structure discovery for spatio-temporal data. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 236–250. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05810-8_16
Qi, L., Zhou, M., Luan, W.: A Two-level traffic light control strategy for preventing incident-based urban traffic congestion. IEEE Trans. Intell. Transp. Syst. 19(1), 13–24 (2016)
Keler, A., Krisp, J.M., Ding, L.: Detecting traffic congestion propagation in urban environments – a case study with floating taxi data (ftd) in shanghai. J. Locat. Based Serv. 11(2), 133–151 (2017)
Liang Z., Chen H., Song Z., et al.: Traffic congestion incident detection and dissipation algorithm for urban intersection based on FCD. In: IEEE International Conference on Computer and Communications, pp. 2578–2583. IEEE press (2017)
Celik, M.: Discovering partial spatio-temporal co-occurrence patterns. In: 1st International Conference on Spatial Data Mining and Geographical Knowledge Services, pp. 116–120. Fuzhou (2011)
Yoo, J., Bow, M.: Mining maximal co-located event sets. In: 15th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 351–362. Elsevier (2011)
Wang, L., Bao, X., Chen, H., et al.: Effective lossless condensed representation and discovery of spatial co-location patterns. Inf. Sci. 436, 197–213 (2018)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), the Natural Science Foundation of Yunnan Province (2015FB114, 2016FA026), and the Project of Innovative Research Team of Yunnan Province (2018HC019).
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He, Y., Wang, L., Fang, Y., Li, Y. (2018). Discovering Congestion Propagation Patterns by Co-location Pattern Mining. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_5
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DOI: https://doi.org/10.1007/978-3-030-01298-4_5
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