Skip to main content
SpringerLink
Log in

Imputation Methods Used in Missing Traffic Data: A Literature Review

  • Conference paper
  • First Online:
Artificial Intelligence Algorithms and Applications (ISICA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1205))

Included in the following conference series:

Abstract

The missing traffic data has caused great obstacles and interference to further research, such as traffic flow prediction, which affects the traffic authorities’ judgment for the real traffic operation state of road network and the new control strategies. It is very critical to select the imputation methods with good performance for maintaining the integrity and effectiveness of the traffic data. A large number of literatures have developed many methods to repair missing traffic data, yet lacking systematic comparison of these methods and an overview of the state-of-the-art development in imputation methods. In this paper, extensive research on imputation methods are sorted out and synthesized, the mechanism of missing traffic data is analyzed, and various algorithms in repairing missing data are systematically reviewed, highlighted some challenges and potential solutions. The purpose is to provide a structural diagram of the current recovery technology for missing traffic data, clearly pointing out the advantages and disadvantages of these methods, and helping researchers to conduct better exploration on the incomplete traffic data.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

References

  1. Tan, H., Feng, G., Feng, J., et al.: A tensor-based method for missing traffic data completion. Transp. Res. Part C Emerg. Technol. 28, 15–27 (2013)

    Article  Google Scholar 

  2. Chen, X., He, Z., Sun, L.: A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transp. Res. Part C Emerg. Technol. 98, 73–84 (2019). https://doi.org/10.1016/j.trc.2018.11.003

    Article  Google Scholar 

  3. Zhang, J., Wang, F.Y., Wang, K., et al.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011). https://doi.org/10.1109/TITS.2011.2158001

    Article  Google Scholar 

  4. Chen, C., Wang, Y., Li, L., Hu, J., Zhang, Z.: The retrieval of intra-day trend and its influence on traffic prediction. Transp. Res. Part C Emerg. Technol. 22, 103–118 (2012). https://doi.org/10.1016/j.trc.2011.12.006

    Article  Google Scholar 

  5. Al-Deek, H.M., 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, 116–126 (2004). https://doi.org/10.3141/1867-14

    Article  Google Scholar 

  6. Qu, L., Li, L., Zhang, Y., Hu, J.: PPCA-based missing data imputation for traffic flow volume: a systematical approach. IEEE Trans. Intell. Transp. Syst. 10(3), 512–522 (2009). https://doi.org/10.1109/TITS.2009.2026312

    Article  Google Scholar 

  7. 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(9), 108–120 (2013). https://doi.org/10.1016/j.trc.2013.05.008

    Article  Google Scholar 

  8. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C Emerg. Technol. 43, 3–19 (2014)

    Article  Google Scholar 

  9. Schafer, J.L.: Analysis of Incomplete Multivariate Data. CRC Press, Boca Raton (1997)

    Book  Google Scholar 

  10. Buuren, S.V.: Flexible Imputation of Missing Data. CRC Press, Boca Raton (2012)

    Book  Google Scholar 

  11. Arteaga, F., Ferrer, A.: Dealing with missing data in MSPC: several methods, different interpretations, some examples. J. Chemom. 16(8–10), 408–418 (2002)

    Article  Google Scholar 

  12. Kondrashov, D., Ghil, M.: Spatio-temporal filling of missing points in geophysical data sets. Nonlinear Process. Geophys. 13(2), 151–159 (2006)

    Article  Google Scholar 

  13. Sainani, K.L.: Dealing with missing data. PM&R 7(9), 990–994 (2015)

    Article  Google Scholar 

  14. García-Laencina, P.J., et al.: Pattern classification with missing data: a review. Neural Comput. Appl. 19(2), 263–282 (2010). https://doi.org/10.1007/s00521-009-0295-6

    Article  Google Scholar 

  15. Li, L., Li, Y., Li, Z.: Missing traffic data: comparison of imputation methods. IET Intell. Transp. Syst. 8(1), 51–57 (2014). https://doi.org/10.1049/iet-its.2013.0052

    Article  Google Scholar 

  16. Tak, S., Woo, S., Yeo, H.: Data-driven imputation method for traffic data in sectional units of road links. IEEE Trans. Intell. Transp. Syst. 17(6), 1762–1771 (2016). https://doi.org/10.1109/TITS.2016.2530312

    Article  Google Scholar 

  17. Sun, B., Ma, L., et al.: An improved k-nearest neighbours method for traffic time series imputation. In: 2017 Chinese Automation Congress (CAC), pp. 7346–7351. IEEE (2017)

    Google Scholar 

  18. Zefreh, M.M., Torok, A.: Single loop detector data validation and imputation of missing data. Measurement 116, 193–198 (2018). https://doi.org/10.1016/j.measurement.2017.10.066

    Article  Google Scholar 

  19. Zou, H., Yue, Y., Li, Q., Yeh, A.G.O.: An improved distance metric for the interpolation of link-based traffic data using kriging: a case study of a large-scale urban road network. Int. J. Geogr. Inf. Sci. 26, 667–689 (2012)

    Article  Google Scholar 

  20. Shamo, B., Asa, E., Membah, J.: Linear spatial interpolation and analysis of annual average daily traffic data. J. Comput. Civil Eng. 29, 04014022 (2015)

    Article  Google Scholar 

  21. Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. EEE Trans. Pattern Anal. Mach. Intell. 35(1), 208–220 (2013)

    Article  Google Scholar 

  22. Asif, M.T., Mitrovic, N., Dauwels, J., Jaillet, P.: Matrix and tensor based methods for missing data estimation in large traffic networks. IEEE Trans. Intell. Transp. Syst. 17(7), 1816–1825 (2016). https://doi.org/10.1109/TITS.2015.2507259

    Article  Google Scholar 

  23. Ran, B., Tan, H., Wu, Y., Jin, P.J.: Tensor based missing traffic data completion with spatial-temporal correlation. Phys. Stat. Mech. Appl. 446, 54–63 (2016)

    Article  Google Scholar 

  24. Goulart, J.H.M., Kibangou, A.Y., Favier, G.: Traffic data imputation via tensor completion based on soft thresholding of Tucker core. Transp. Res. Part C Emerg. Technol. 85, 348–362 (2017). https://doi.org/10.1016/j.trc.2017.09.011

    Article  Google Scholar 

  25. Chen, X., He, Z., Wang, J.: Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transp. Res. Part C Emerg. Technol. 86, 59–77 (2018). https://doi.org/10.1016/j.trc.2017.10.023

    Article  Google Scholar 

  26. Payne, H.J., Helfenbein, E.D., Knobel, H.C.: Development and testing of incident detection algorithms, volume 2: research methodology and detailed results. Federal Highway Administration, Washington, D.C. (1976)

    Google Scholar 

  27. Jacobson, L.N., Nihan, N.L., Bender, J.D.: Detecting erroneous loop detector data in a freeway traffic management system. Transp. Res. Rec. (1287), 151–166 (1990)

    Google Scholar 

  28. Rubin, D.B.: Inference and missing data. Biometrika 63, 581–592 (1976)

    Article  MathSciNet  Google Scholar 

  29. Tang, J., Zhang, G., Wang, Y., Wang, H., Liu, F.: A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transp. Res. Part C Emerg. Technol. 51, 29–40 (2015)

    Article  Google Scholar 

  30. Duan, Y., Lv, Y., Liu, Y.L., Wang, F.Y.: An efficient realization of deep learning for traffic data imputation. Transp. Res. Part C Emerg. Technol. 72, 168–181 (2016)

    Article  Google Scholar 

  31. Pigott, T.D.: A review of methods for missing data. Educ. Res. Eval. 7(4), 353–383 (2001). https://doi.org/10.1076/edre.7.4.353.8937

    Article  Google Scholar 

  32. Yin, W., Murray-Tuite, P., Rakha, H.: Imputing erroneous data of single-station loop detectors for nonincident conditions: comparison between temporal and spatial methods. J. Intell. Transp. Syst. 16(3), 159–176 (2012)

    Article  Google Scholar 

  33. Xu, J.R., Li, X.Y., Shi, H.J.: Short-term traffic flow forecasting model under missing data. J. Comput. Appl. 30, 1117–1120 (2010)

    Google Scholar 

  34. Lee, S., Fambro, D.B.: Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp. Res. Rec. J. Transp. Res. Board 1678, 179–188 (1999)

    Article  Google Scholar 

  35. Castro-Neto, M., Jeong, Y.S., Jeong, M.K., et al.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36, 6164–6173 (2009). https://doi.org/10.1016/j.eswa.2008.07.069

    Article  Google Scholar 

  36. Chiou, J.M., Zhang, Y.C., Chen, W.H., et al.: A functional data approach to missing value imputation and outlier detection for traffic flow data. Transp. B Transp. Dyn. 2(2), 106–129 (2014). https://doi.org/10.1080/21680566.2014.892847

    Article  Google Scholar 

  37. Tan, H., Feng, J., Chen, Z., et al.: Low multilinear rank approximation of tensors and application in missing traffic data. Adv. Mech. Eng (2014). https://doi.org/10.1155/2014/157597

    Article  Google Scholar 

  38. Anandkumar, A., Ge, R., Hsu, D., Kakade, S.M., Telgarsky, M.: Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15, 2773–2832 (2014)

    MathSciNet  MATH  Google Scholar 

  39. Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. J. Math. Phys. 6, 164–189 (1927). https://doi.org/10.1002/sapm192761164

    Article  MATH  Google Scholar 

  40. Tucker, L.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279–311 (1966)

    Article  MathSciNet  Google Scholar 

  41. Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika 35, 283–319 (1970)

    Article  Google Scholar 

  42. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009). https://doi.org/10.1137/07070111X

    Article  MathSciNet  MATH  Google Scholar 

  43. Schifanella, C., Candan, K.S., Sapino, M.L.: Multiresolution tensor decompositions with mode hierarchies. ACM Trans. Knowl. Discov. Data 8(2), 10 (2014)

    Article  Google Scholar 

  44. Acar, E., Dunlavy, D.M., Kolda, T.G., Mørup, M.: Scalable tensor factorizations for incomplete data. Chemom. Intell. Lab. Syst. 106(1), 41–56 (2011)

    Article  Google Scholar 

  45. Zhao, Q., Zhang, L., Cichocki, A.: Bayesian CP factorization of incomplete tensors with automatic rank determination. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1751–1763 (2015). https://doi.org/10.1109/TPAMI.2015.2392756

    Article  Google Scholar 

  46. Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 374–383 ACM (2014)

    Google Scholar 

  47. Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning (ICML) (2008). https://doi.org/10.1145/1390156.1390267

  48. Xiong, L., Chen, X., Huang, T.K., Schneider, J., Carbonell, J.G.: Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: SIAM International Conference on Data Mining, pp. 211–222 (2010). https://doi.org/10.1137/1.9781611972801.19

  49. Rai, P., Wang, Y., Guo, S., Chen, G., Dunson, D., Carin, L.: Scalable Bayesian low-rank decomposition of incomplete multiway tensors. In: Proceedings of the 31st International Conference on Machine Learning (ICML), vol. 32, pp. 1800–1808 (2014)

    Google Scholar 

  50. Tan, H., Wu, Y., Shen, B., Jin, P.J., Ran, B.: Short-term traffic prediction based on dynamic tensor completion. IEEE Trans. Intell. Transp. Syst. 17(8), 2123–2133 (2016)

    Article  Google Scholar 

  51. De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)

    Article  MathSciNet  Google Scholar 

  52. Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009). https://doi.org/10.1007/s10208-009-9045-5

    Article  MathSciNet  MATH  Google Scholar 

  53. Cai, J.F., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010). https://doi.org/10.1137/080738970

    Article  MathSciNet  MATH  Google Scholar 

  54. Gandy, S., Recht, B., Yamada, I.: Tensor completion and low-n-rank tensor recovery via convex optimization. Inverse Probl. 27(2), 1–20 (2011). https://doi.org/10.1088/0266-5611/27/2/025010

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong, China

    Pan Wu, Lunhui Xu & Zilin Huang

Authors
  1. Pan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lunhui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zilin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lunhui Xu .

Editor information

Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, China

    Wei Li

  3. South China Agricultural University, Guangzhou, China

    Hui Wang

  4. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, P., Xu, L., Huang, Z. (2020). Imputation Methods Used in Missing Traffic Data: A Literature Review. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_53

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5577-0_53

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5576-3

  • Online ISBN: 978-981-15-5577-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation