Many recent applications of intelligent transportation systems require both real-time and network-wide traffic flow data as input. However, as the detection time and network size increase, the data volume may become very large in terms of both dimension and scale. To address this concern, various traffic flow data compression methods have been proposed, which archive the low-dimensional subspace rather than the original data. Many studies have shown the traffic flow data consist of different components, i.e. low-dimensional intra-day trend, Gaussian type fluctuation and burst components. Existing compression methods cannot compress the burst components well and provide very limited choices of compression ratio (CR). A better compression method should have the ability to archive all the dominant information in different components of traffic flow data. In this study, the authors compare the influence of different data reformatting, archive the bursts defined before in descending order with respect to the absolute value of the burst points and propose a flexible compression framework to balance between burst components and low-dimensional intra-day trend. Experimental results show that the proposed framework promotes the reconstruction accuracy significantly. Moreover, the proposed framework provides more flexible choices with respect to CR, which can benefit a variety of applications.

References

1. 1)
  - 10. Ou, X., Ren, J., Zhang, Y.: ‘A neural network model for urban volumes compression’. The Sixth World Multicongress on Systemics, Cybernetics and Informations, Orlando, FL, June 2002.
2. 2)
  - 21. Chen, C., Wang, Y., Li, L., et al: ‘The retrieval of intra-day trend and its influence on traffic prediction’, Transp. Res. C, 2012, 22, pp. 103–118.
3. 3)
  - 6. Shawe-Taylor, J., De Bie, T., Cristianini, N.: ‘Data mining, data fusion and information management’, Proc. Inst. Electr. Eng., Intell. Transp. Syst., 2006, 153, (3), pp. 221–229.
4. 4)
  - 5. ‘Performance measurement system (PeMS)’. Available at http://pems.dot.ca.gov/, accessed February 2016.
5. 5)
  - 26. Asadi, R., Ghatee, M.: ‘A rule-based decision support system in intelligent hazmat transportation system’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 2756–2764.
6. 6)
  - 18. Cover, T.M., Thomas, J.A.: ‘Elements of information theory’ (Wiley-Interscience Press, 2006, 2nd edn.).
7. 7)
  - 17. Huffman, D.A.: ‘A method for the construction of minimum-redundancy codes’. Proc. I.R.E., p. 1098.
8. 8)
  - 22. Li, L., Su, X., Wang, Y., et al: ‘Robust causal dependence mining in big data network and its application to traffic flow predictions’, Transp. Res. C, 2015, 58, pp. 292–307.
9. 9)
  - 19. Berger, T.: ‘Rate distortion theory: mathematical basis for data compression’, 1971.
10. 10)
  - 3. Asif, M.T., Kannan, S., Dauwels, J., et al: ‘Data compression techniques for urban traffic data’. 2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS), April 2013, pp. 44–49.
11. 11)
  - 23. Li, L., Li, Z., Zhang, Y., et al: ‘A mixed-fractal traffic flow model whose Hurst exponent appears crossover’. Fifth Int. Joint Conf. Computational Sciences and Optimization, 2012, pp. 443–447.
12. 12)
  - 14. Cichocki, A., Mandic, D., De Lathauwer, L., et al: ‘Tensor decompositions for signal processing applications’, IEEE Signal Process. Mag., 2015, 32, (2), pp. 145–163.
13. 13)
  - 12. Steeb, W.H., Hardy, Y.: ‘Matrix calculus and Kronecker product: a practical approach to linear and multilinear algebra’, World Sci., 2011.
14. 14)
  - 13. De Lathauwer, L., De Moor, B., Vandewalle, J.: ‘on the best rank-1 and rank-(r 1, r 2, …, rn) approximation of higher-order tensors’, SIAM J. Matrix Anal. Appl., 2000, 21, (4), pp. 1324–1342.
15. 15)
  - 11. Yang, X., Yu-ming, X., Lingyun, L., et al: ‘The traffic data compression and decompression for intelligent traffic systems based on two-dimensional wavelet transformation’. Seventh Int. Conf. Signal Processing, Beijing, China, 31 August–4 September 2004, pp. 2560–2563.
16. 16)
  - 25. Eftekhari, H.R., Ghatee, M.: ‘An inference engine for smartphones to preprocess data and detect stationary and transportation modes’, Transp. Res. C, Emerging Technol., 2016, 69, pp. 313–327.
17. 17)
  - 15. Salomon, D.: ‘Data compression: the complete reference’ (Springer Science & Business Media, 2004).
18. 18)
  - 20. Li, L., Su, X., Zhang, Y., et al: ‘Trend modeling for traffic time series analysis: an integrated study’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (6), pp. 3430–3439.
19. 19)
  - 9. Shuo, F., Yi, Z., Li, L.: ‘A comparison study for traffic flow data comparison’. 2016 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 12–15 June 2016, pp. 977–982.
20. 20)
  - 2. Ke, R., Li, Z., Kim, S., et al: ‘Real-time bidirectional traffic flow parameter estimation from aerial videos’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (4), pp. 890–901.
21. 21)
  - 7. Qu, L., Hu, J.M., Zhang, Y.: ‘A flow volume data compression approach for traffic network based on principal component analysis’. Proc. 2007 IEEE Intelligent Transportation Systems Conf., Seattle, WA, USA, 2007, pp. 125–130.
22. 22)
  - 16. Shannon, C.E.: ‘A mathematical theory of communication’, Bell Syst. Tech. J., 1952, 27, pp. 379–423, 623-656, July, October, 1948-1101.
23. 23)
  - 8. Jun, D., Zuo, Z, Xiao, M.: ‘A method for urban traffic data compression based on wavelet-PCA’. Fourth Int. Joint Conf. Computational Sciences and Optimization, 2011, pp. 1030–1034.
24. 24)
  - 4. Leduc, G.: ‘Road traffic data: collection methods and applications’, Working Papers Energy Transp. Clim. Change, 2008, 1, (55).
25. 25)
  - 24. Chen, Y., Li, L., Zhang, Y., et al: ‘Fluctuations in urban traffic networks’, Mod. Phys. Lett. B, 2011, 22, (2), pp. 101–115.
26. 26)
  - 1. Zhang, J., Wang, F.Y., Wang, K., et al: ‘Data-driven intelligent transportation systems: a survey’, IEEE Transp. Intell. Syst., 2011, 12, pp. 1624–1639.

Traffic flow data compression considering burst components

References

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