Spatio-Temporal Clustering of Traffic Data with Deep Embedded Clustering

ABSTRACT

Traffic data is a challenging spatio-temporal data, and a multivariate time series data with spatial similarities. Clustering of traffic data is a fundamental tool for various machine learning tasks including anomaly detection, missing data imputation and short term forecasting problems. In this paper, first, we formulate a spatio-temporal clustering problem and define temporal and spatial clusters. Then, we propose an approach for finding temporal and spatial clusters with a deep embedded clustering model. The proposed approach is examined on traffic flow data. In the analysis, we present the properties of clusters and patterns in the dataset. The analysis shows that the temporal and spatial clusters have meaningful relationships with temporal and spatial patterns in traffic data, and the clustering method effectively finds similarities in traffic data.

References

1. G. Atluri, A. Karpatne, and V. Kumar, "Spatio-temporal data mining: A survey of problems and methods," ACM Computing Surveys (CSUR), vol. 51, no. 4, p. 83, 2018.
2. A. M. Nagy and V. Simon, "Survey on traffic prediction in smart cities," Pervasive and Mobile Computing, vol. 50, pp. 148--163, 2018.
3. F. Rempe, G. Huber, and K. Bogenberger, "Spatio-temporal congestion patterns in urban traffic networks," Transportation Research Procedia, vol. 15, pp. 513--524, 2016.
4. L. Zhu, F. R. Yu, Y. Wang, B. Ning, and T. Tang, "Big data analytics in intelligent transportation systems: A survey," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 383--398, 2018.
5. C. Wang, X. Li, X. Zhou, A. Wang, and N. Nedjah, "Soft computing in big data intelligent transportation systems," Applied Soft Computing, vol. 38, pp. 1099--1108, 2016.
6. M. Ahmed and A. N. Mahmood, "Novel approach for network traffic pattern analysis using clustering-based collective anomaly detection," Annals of Data Science, vol. 2, no. 1, pp. 111--130, 2015.
7. W. C. Ku, G. R. Jagadeesh, A. Prakash, and T. Srikanthan, "A clustering-based approach for data-driven imputation of missing traffic data," in 2016 IEEE Forum on Integrated and Sustainable Transportation Systems (FISTS), pp. 1--6, IEEE, 2016.
8. R. Asadi and A. Regan, "A spatial-temporal decomposition based deep neural network for time series forecasting," arXiv preprint arXiv:1902.00636, 2019.
9. M. Y. Choong, L. Angeline, R. K. Y. Chin, K. B. Yeo, and K. T. K. Teo, "Vehicle trajectory clustering for traffic intersection surveillance," in 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1--4, IEEE, 2016.
10. H. B. Celikoglu and M. A. Silgu, "Extension of traffic flow pattern dynamic classification by a macroscopic model using multivariate clustering," Transportation Science, vol. 50, no. 3, pp. 966--981, 2016.
11. S. Kisilevich, F. Mansmann, M. Nanni, and S. Rinzivillo, "Spatio-temporal clustering," in Data mining and knowledge discovery handbook, pp. 855--874, Springer, 2009.
12. H. Chunchun, L. Nianxue, Y. Xiaohong, and S. Wenzhong, "Traffic flow data mining and evaluation based on fuzzy clustering techniques.," International Journal of Fuzzy Systems, vol. 13, no. 4, 2011.
13. M. A. Silgu and H. B. Celikoglu, "Clustering traffic flow patterns by fuzzy c-means method: some preliminary findings," in International Conference on Computer Aided Systems Theory, pp. 756--764, Springer, 2015.
14. X. Huang, Y. Ye, L. Xiong, R. Y. Lau, N. Jiang, and S. Wang, "Time series k-means: A new k-means type smooth subspace clustering for time series data," Information Sciences, vol. 367, pp. 1--13, 2016.
15. J. Tang, G. Zhang, Y. Wang, H. Wang, and F. Liu, "A hybrid approach to integrate fuzzy c-means based imputation method with genetic algorithm for missing traffic volume data estimation," Transportation Research Part C: Emerging Technologies, vol. 51, pp. 29--40, 2015.
16. S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah, "Time-series clustering--a decade review," Information Systems, vol. 53, pp. 16--38, 2015.
17. S. Soheily-Khah, A. Douzal-Chouakria, and E. Gaussier, "Generalized k-means-based clustering for temporal data under weighted and kernel time warp," Pattern Recognition Letters, vol. 75, pp. 63--69, 2016.
18. J. Paparrizos and L. Gravano, "Fast and accurate time-series clustering," ACM Transactions on Database Systems (TODS), vol. 42, no. 2, p. 8, 2017.
19. S. Wang, J. Cao, and P. S. Yu, "Deep learning for spatio-temporal data mining: A survey," arXiv preprint arXiv:1906.04928, 2019.
20. R. Asadi and A. Regan, "A convolution recurrent autoencoder for spatio-temporal missing data imputation," arXiv preprint arXiv:1904.12413, 2019.
21. E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long, "A survey of clustering with deep learning: From the perspective of network architecture," IEEE Access, vol. 6, pp. 39501--39514, 2018.
22. J. Xie, R. Girshick, and A. Farhadi, "Unsupervised deep embedding for clustering analysis," in International conference on machine learning, pp. 478--487, 2016.
23. X. Guo, L. Gao, X. Liu, and J. Yin, "Improved deep embedded clustering with local structure preservation.," in IJCAI, pp. 1753--1759, 2017.
24. B. Yang, X. Fu, N. D. Sidiropoulos, and M. Hong, "Towards k-means-friendly spaces: Simultaneous deep learning and clustering," in Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 3861--3870, JMLR. org, 2017.
25. P. Tzirakis, M. A. Nicolaou, B. Schuller, and S. Zafeiriou, "Time-series clustering with jointly learning deep representations, clusters and temporal boundaries,"
26. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," Journal of machine learning research, vol. 11, no. Dec, pp. 3371--3408, 2010.
27. "California. pems, http://pems.dot.ca.gov/, 2017,"
28. L. v. d. Maaten and G. Hinton, "Visualizing data using t-sne," Journal of machine learning research, vol. 9, no. Nov, pp. 2579--2605, 2008.

Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Neural networks

• Information systems

  • Information systems applications

    • Spatial-temporal systems