access icon free Compression algorithm of road traffic data in time series based on temporal correlation

The numerous applications of urban traffic detection technology in road traffic data acquisition bring new challenges for transportation and storage of road traffic big data. The travel demand and travel time of travel participants present certain specific regularity; thus, a compression algorithm for road traffic data in time series based on temporal correlation was proposed in this study. First, the temporal correlation of the road traffic data in time series was analysed. Second, the reference sequences of road traffic characteristics were constructed to acquire the base data under different modes. Third, the training data under the same mode were extracted to acquire the difference data between training and base data. Then the optimal threshold of the difference data was trained. Fourth, the optimal threshold was introduced into the difference data between real-time and base data in time series, combining with Lempel-Ziv-Welch (LZW) encoding to achieve the compression of difference data. Finally, the reconstruction of real-time road traffic data in time series was accomplished based on LZW decoding technology. Six typical road segments in Beijing were adopted for case studies. The final results prove the feasibility of the algorithm, and that the reconstructed data can achieve high accuracy.

Inspec keywords: decoding; road traffic; time series; data compression; traffic engineering computing

Other keywords: road traffic data; data reconstruction; Beijing; Lempel–Ziv–Welch encoding; compression algorithm; LZW decoding technology; temporal correlation; time series compression algorithm

Subjects: Traffic engineering computing; Data handling techniques; Other topics in statistics

References

    1. 1)
      • 21. Li, L., Su, X., Zhang, Y., et al: ‘Traffic prediction, data compression, abnormal data detection and missing data imputation: an integrated study based on the decomposition of traffic time series’. Intelligent Transportation Systems (ITSC), Qingdao, China, October 2014, pp. 282289.
    2. 2)
      • 22. Mitrovic, N., Asif, M., Rasheed, U., et al: ‘CUR decomposition for compression and compressed sensing of large-scale traffic data’. Intelligent Transportation Systems (ITSC), Hague, Netherlands, October 2013, pp. 14751480.
    3. 3)
      • 17. Ma, Q.L., Liu, W. N., Sun, D.: ‘H. A high-speed compression scheme for vast quantities of GPS data’, J. Sichuan Univeristy, 2011, 43, (1), pp. 123128(in Chinese).
    4. 4)
      • 3. Gijzen, H.: ‘Development: big data for a sustainable future’, Nature, 2013, 502, (7469), pp. 3838.
    5. 5)
      • 2. Meng, X. F., Ci, X.: ‘Big data management: concepts, techniques and challenges’, J. Comput. Res. Dev., 2013, 1, p. 98.
    6. 6)
      • 1. Goldston, D.: ‘Big data: data wrangling’, Nature News, 2008, 455, (15).
    7. 7)
      • 11. Li, Q.Q., Zhou, Y., Le, Y., et al: ‘Compression method of traffic flow data based on compressed sensing’, J. Traffic Transp. Eng., 2012, 12, (3), pp. 113119.
    8. 8)
      • 6. Sayood, K.: ‘Introduction to data compression’ (Elsevier Press, Cambridge, 2012, 4th edn.), Newnes.
    9. 9)
      • 18. Wang, Q., Wang, K., Yang, Z.: ‘Coding algorithm of traffic flow in intelligence guidance system based on adaptive switching mode’, China J. Highw. Transp., 2009, 6, pp. 14(in Chinese).
    10. 10)
      • 8. Yao, X., Zhan, F., Lu, Y., et al: ‘Effects of real-time traffic information systems on traffic performance under different network structures’, J. Central South University, 2012, 19, (2), pp. 586592.
    11. 11)
      • 10. Xu, D.W., Dong, H. H., Li, H.J., et al: ‘The estimation of road traffic states based on compressive sensing’, Transportmetrica B, Transp. Dyn., 2015, 3, (2), pp. 131152.
    12. 12)
      • 20. Song, R., Sun, W., Zheng, B., et al: ‘RESS: a novel framework of trajectory compression in road networks’, Proc. VLDB Endowment, 2014, 7, (9), pp. 661672.
    13. 13)
      • 5. Shi, Q., Abdel-Aty, M.: ‘Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways’, Transp. Res.C, Emerging Technol., 2015, 58, pp. 380394.
    14. 14)
      • 16. Xu, G.J., Wang, H.: ‘Implementation of locating data real-time compression of embedded GPS system with car’, Inf. Technol., 2006, 4, p. 14(in Chinese).
    15. 15)
      • 24. Saltelli, A., Ratto, M., Andres, T., et al: ‘Global sensitivity analysis: the primer’ (John Wiley & Sons Press, England, 2008).
    16. 16)
      • 9. Zhao, Z.Q., Zhang, Y., Hu, J.M., et al: ‘Comparative study of PCA and ICA based traffic flow compression’, J. Highw. Transp. Res. Dev., 2008, 25, (11), pp. 109113(in Chinese).
    17. 17)
      • 13. Li, B., Xie, J.Z., Wang, B.L.: ‘Signal reconstruction based on compressed sensing’, Comput. Technol. Dev., 2009, 19, (5), pp. 2325.
    18. 18)
      • 14. Xiao, Y., Lu, L., Y., Gao S, , et al: ‘Traffic data denoising compression for intelligent traffic system based on 2_D discrete wavelet transformation’, J. Beijing Jiaotong Univ., 2015, 28, (5), pp. 15.
    19. 19)
      • 15. Ou, X.L., Ren, J.T., Zhang, Y.: ‘A neural network mode for urban volumes compression’, The 6th World Multicongress on Systemics, Cybernetics and Informations, 2003, pp. 14781480.
    20. 20)
      • 4. Vlahogianni, E. I., Park, B. B., van Lint, J. W. C.: ‘Big data in transportation and traffic engineering’, Transp. Res. C, Emerging Technol., 2015, 58, p. 161.
    21. 21)
      • 19. Hou, M.: ‘QoS management with differentiated services IP over the internet’. Kingston, Ontario, Canada, 1999.
    22. 22)
      • 23. Saltelli, A.: ‘Sensitivity analysis for importance assessment’, Risk Anal., 2002, 22, (3), pp. 579590.
    23. 23)
      • 12. Duarte, M.F., Eldar, Y.C.: ‘Structured compressed sensing: from theory to applications’, IEEE Trans. Signal Process., 2011, 59, (9), pp. 40534085.
    24. 24)
      • 7. Ahn, G., Ki, Y., Kim, E.: ‘Real-time estimation of travel speed using urban traffic information system and filtering algorithm’, IET Intell. Transp. Syst., 2014, 8, (2), pp. 145154.
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