Skip to main content
SpringerLink
Log in

Estimating the Missing Traffic Speeds via Continuous Conditional Random Fields

  • Conference paper
  • First Online:
Web Technologies and Applications (APWeb 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9461))

Included in the following conference series:

  • 626 Accesses

Abstract

Recently, increasing interests have been emerging in the data driven intelligent transportation systems [27], some typical applications include flock pattern recognition, road network structure inference and route searching. However, almost all the applications suffer from the missing data problem. In this paper, we propose to adopt the Continuous Conditional Random Fields (CCRFs) model [24] to estimate the missing historical traffic data. We exam the proposed method with a real traffic speed dataset, results show that it is superior to the comparison algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Google Map. https://maps.google.com/, visited May 2015

  2. Al-Deek, H., Venkata, C., Chandra, S.R.: New algorithms for filtering and imputation of real-time and archived dual-loop detector data in I-4 data warehouse. Transp. Res. Rec. J. Transp. Res. Board 1867(1), 116–126 (2004)

    Article  Google Scholar 

  3. Andersson, M., Gudmundsson, J., Laube, P., Wolle, T.: Reporting leaders and followers among trajectories of moving point objects. Geoinformatica 12(4), 497–528 (2008)

    Article  Google Scholar 

  4. Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Comput. Geom. Theory Appl. 41(3), 111–125 (2008)

    Article  MATH  Google Scholar 

  5. Bruntrup, R., Edelkamp, S., Jabbar, S., Scholz, B.: Incremental map generation with gps traces. In: Proceedings of Intelligent Transportation Systems, pp. 574–579. IEEE (2005)

    Google Scholar 

  6. Cao L., Krumm, J.: From GPS traces to a routable road map. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 3–12. ACM (2009)

    Google Scholar 

  7. Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity detection in time series databases. IEEE Trans. Knowl. Data Eng. 17(7), 875–887 (2005)

    Article  Google Scholar 

  8. Fan, R.C., Yang, X., Fay, J.D.: Using location data to determine traffic information, 15 July 2003. US Patent 6,594,576

    Google Scholar 

  9. Fathi, A., Krumm, J.: Detecting road intersections from GPS traces. In: Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., Schlieder, C. (eds.) GIScience 2010. LNCS, vol. 6292, pp. 56–69. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Gold, D.L., Turner, S.M., Gajewski, B.J., Spiegelman, C.: Imputing missing values in ITS data archives for intervals under 5 minutes. Presented at the 80th Annual Meeting of the Transportation Research Board, Washington, DC (2000)

    Google Scholar 

  11. Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.P.: Adaptive fastest path computation on a road network: a traffic mining approach. In: Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB 2007, pp. 794–805. VLDB Endowment (2007)

    Google Scholar 

  12. Gudmundsson, J., van Kreveld, M.: Computing longest duration flocks in trajectory data. In: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems, GIS 2006, pp. 35–42. ACM, New York (2006)

    Google Scholar 

  13. He, X., Zemel, R.S., Carreira-Perpindn, M.: Multiscale conditional random fields for image labeling. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II-695. IEEE (2004)

    Google Scholar 

  14. Huang, X.-Y., Li, W., Chen, K., Xiang, X.-H., Pan, R., Li, L., Cai, W.-X.: Multi-matrices factorization with application to missing sensor data imputation. Sensors 13(11), 15172–15186 (2013)

    Article  Google Scholar 

  15. Huang, X.-Y., Xiang, X.-H., Li, W., Chen, K., Cai, W.-X., Li, L.: Matrix factorization for evolution data. Math. Probl. Eng. 2014 (2014)

    Google Scholar 

  16. Huber, W., Lädke, M., Ogger, R.: Extended floating-car data for the acquisition of traffic information. In: Proceedings of the 6th World Congress on Intelligent Transport Systems, pp. 1–9 (1999)

    Google Scholar 

  17. Kanoulas, E., Du, Y., Xia, T., Zhang, D.: Finding fastest paths on a road network with speed patterns. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, p. 10. IEEE Computer Society, Washington (2006)

    Google Scholar 

  18. Kwon, T.M.: TMC traffic data automation for Mn/DOT’s traffic monitoring program. Technical report MN/RC-2004-29, Minnesota Department of Transportation (2004)

    Google Scholar 

  19. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001)

    Google Scholar 

  20. Li, L., Li, Y., Li, Z.: Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transp. Res. Part C: Emerg. Technol. 34, 108–120 (2013)

    Article  Google Scholar 

  21. Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 1099–1108. ACM, New York (2010)

    Google Scholar 

  22. Liao, L., Fox, D., Kautz, H.: Extracting places and activities from gps traces using hierarchical conditional random fields. Int. J. Rob. Res. 26(1), 119–134 (2007)

    Article  Google Scholar 

  23. Nguyen, L.N., Scherer, W.T.: Imputation techniques to account for missing data in support of intelligent transportation systems applications.Technical report UVACTS-13-0-78, University of Virginia, Center for Transportation Studies (2003)

    Google Scholar 

  24. Qin, T., Liu, T.-Y., Zhang, X.-D., Wang, D.-S., Li, H.: Global ranking using continuous conditional random fields. In: Advances in Neural Information Processing Systems, pp. 1281–1288 (2009)

    Google Scholar 

  25. Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 134–141. Association for Computational Linguistics (2003)

    Google Scholar 

  26. Shuai, M., Xie, K., Pu, W., Song, G., Ma, X.: An online approach based on locally weighted learning for short-term traffic flow prediction. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2008, pp. 45:1–45:4. ACM, New York (2008)

    Google Scholar 

  27. Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)

    Article  Google Scholar 

Download references

Acknowledgments

The research was partially supported by the National High Technology Research and Development Program (863) of China (NO. 2012AA12A203).

Author information

Authors and Affiliations

  1. Academy of Guangdong Telecom Co. Ltd., Guangzhou, 510630, China

    Bing Liang, Chong Chen & Ying-Hui Guan

  2. School of Economics and Commerce, South China University of Technology, Guangzhou, 510006, China

    Xiao-Yu Huang

Authors
  1. Bing Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying-Hui Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiao-Yu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Liang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, Guangdong, China

    Ruichu Cai

  2. Research Institute of China Telecom Co., Guangzhou, China

    Kang Chen

  3. Wuhan University, Wuhan, China

    Liang Hong

  4. Advanced Digital Sciences Center, Singapore, Singapore

    Xiaoyan Yang

  5. East China Normal University, Shanghai, China

    Rong Zhang

  6. Peking University, Beijing, China

    Lei Zou

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Liang, B., Chen, C., Guan, YH., Huang, XY. (2015). Estimating the Missing Traffic Speeds via Continuous Conditional Random Fields. In: Cai, R., Chen, K., Hong, L., Yang, X., Zhang, R., Zou, L. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9461. Springer, Cham. https://doi.org/10.1007/978-3-319-28121-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28121-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28120-9

  • Online ISBN: 978-3-319-28121-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation