http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

References

    1. 1)
      • 1. Goldston, D.: ‘Big data: data wrangling’, Nature News, 2008, 455, (15).
    2. 2)
      • 2. Meng, X. F., Ci, X.: ‘Big data management: concepts, techniques and challenges’, J. Comput. Res. Dev., 2013, 1, p. 98.
    3. 3)
      • 3. Gijzen, H.: ‘Development: big data for a sustainable future’, Nature, 2013, 502, (7469), pp. 3838.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 6. Sayood, K.: ‘Introduction to data compression’ (Elsevier Press, Cambridge, 2012, 4th edn.), Newnes.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 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).
    10. 10)
      • 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.
    11. 11)
      • 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.
    12. 12)
      • 12. Duarte, M.F., Eldar, Y.C.: ‘Structured compressed sensing: from theory to applications’, IEEE Trans. Signal Process., 2011, 59, (9), pp. 40534085.
    13. 13)
      • 13. Li, B., Xie, J.Z., Wang, B.L.: ‘Signal reconstruction based on compressed sensing’, Comput. Technol. Dev., 2009, 19, (5), pp. 2325.
    14. 14)
      • 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.
    15. 15)
      • 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.
    16. 16)
      • 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).
    17. 17)
      • 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).
    18. 18)
      • 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).
    19. 19)
      • 19. Hou, M.: ‘QoS management with differentiated services IP over the internet’. Kingston, Ontario, Canada, 1999.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 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.
    23. 23)
      • 23. Saltelli, A.: ‘Sensitivity analysis for importance assessment’, Risk Anal., 2002, 22, (3), pp. 579590.
    24. 24)
      • 24. Saltelli, A., Ratto, M., Andres, T., et al: ‘Global sensitivity analysis: the primer’ (John Wiley & Sons Press, England, 2008).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0244
Loading

Related content

content/journals/10.1049/iet-its.2016.0244
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address