Robust and flexible strategy for missing data imputation in intelligent transportation system

Robust and flexible strategy for missing data imputation in intelligent transportation system

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Rich and complete data play a fundamental role in intelligent traffic management and control applications. A great volume of missing data is found in the intelligent transportation system. In this paper, the authors introduce an ensemble strategy to handle the missing values. The proposed strategy is a general framework that different models, whether linear, neural networks, or other, can be applied. In this strategy, missing values are first computed by the forward and backward models, and their results are combined to recover the incomplete raw data. Then, the models are iterated for several times to enhance the accuracy. Three commonly used imputation models are tested in the proposed strategy using the data from real world. The results indicate that the proposed strategy can significantly improve the accuracy of the imputation with different missing types and during different traffic states. Moreover, the increase of the iteration is capable to improve the performance of the models.


    1. 1)
      • 1. vanderVoort, M., Dougherty, M., Watson, S.: ‘Combining Kohonen maps with ARIMA time series models to forecast traffic flow’, Transp. Res. C, Emerg., 1996, 4, (5), pp. 307318.
    2. 2)
      • 2. Sun, H.Y., Liu, H.X., Xiao, H., et al: ‘Use of local linear regression model for short-term traffic forecasting’, Transp. Res. Rec., 2003, (1836), pp. 143150.
    3. 3)
      • 3. Chan, K.Y., Dillon, T.S., Singh, J., et al: ‘Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm’, IEEE Trans. Intell. Transp., 2012, 13, (2), pp. 644654.
    4. 4)
      • 4. Tang, J., Liu, F., Zou, Y., et al: ‘An improved fuzzy neural network for traffic speed prediction considering periodic characteristic’, IEEE Trans. Intell. Transp., 2017, 18, (9), pp. 23402350.
    5. 5)
      • 5. Tang, J., Zou, Y., Ash, J., et al: ‘Travel time estimation using freeway point detector data based on evolving fuzzy neural inference system’, PLOS One, 2016, 11, (2), p. e0147263.
    6. 6)
      • 6. Tang, J., Wang, Y., Liu, F.: ‘Characterizing traffic time series based on complex network theory’, Physica A, 2013, 392, (18), pp. 41924201.
    7. 7)
      • 7. Tang, J., Liu, F., Zhang, W., et al: ‘Exploring dynamic property of traffic flow time series in multi-states based on complex networks: phase space reconstruction versus visibility graph’, Physica A, 2016, 450, pp. 635648.
    8. 8)
      • 8. Wei, D.L., Liu, H.C.: ‘An adaptive-margin support vector regression for short-term traffic flow forecast’, J. Intell. Transp. Syst., 2013, 17, (4), pp. 317327.
    9. 9)
      • 9. Tan, H.C., Feng, G.D., Feng, J.S., et al: ‘A tensor-based method for missing traffic data completion’, Transp. Res. C, Emerg., 2013, 28, pp. 1527.
    10. 10)
      • 10. Ran, B., Song, L., Zhang, J., et al: ‘Using tensor completion method to achieving better coverage of traffic state estimation from sparse floating car data’, PLOS One, 2016, 11, (7).
    11. 11)
      • 11. Ran, B., Tan, H.C., Feng, J.S., et al: ‘Estimating missing traffic volume using low multilinear rank tensor completion’, J Intell. Transp. Syst., 2016, 20, (2), pp. 152161.
    12. 12)
      • 12. Ran, B., Tan, H.C., Wu, Y.K., et al: ‘Tensor based missing traffic data completion with spatial–temporal correlation’, Physica A, 2016, 446, pp. 5463.
    13. 13)
      • 13. Chen, C.Y., Wang, Y., Li, L., et al: ‘The retrieval of intra-day trend and its influence on traffic prediction’, Transp. Res. C, Emerg., 2012, 22, pp. 103118.
    14. 14)
      • 14. Karlaftis, M.G., Vlahogianni, E.I.: ‘Statistical methods versus neural networks in transportation research: differences, similarities and some insights’, Transp. Res. C, Emerg., 2011, 19, (3), pp. 387399.
    15. 15)
      • 15. Qu, L., Li, L., Zhang, Y., et al: ‘PPCA-based missing data imputation for traffic flow volume: a systematical approach’, IEEE Trans. Intell. Transp., 2009, 10, (3), pp. 512522.
    16. 16)
      • 16. Li, Y.B., Li, Z.H., Li, L.: ‘Missing traffic data: comparison of imputation methods’, IET Intell. Transp. Syst., 2014, 8, (1), pp. 5157.
    17. 17)
      • 17. Chen, C., Kwon, J., Rice, J., et al: ‘Detecting errors and imputing missing data for single-loop surveillance systems’, Transp. Res. Rec., 2003, 1855, pp. 160167.
    18. 18)
      • 18. Zhong, M., Sharma, S., Liu, Z.B.: ‘Assessing robustness of imputation models based on data from different jurisdictions – examples of Alberta and Saskatchewan, Canada’, Transp. Res. Rec., 2005, (1917), pp. 116126.
    19. 19)
      • 19. Tang, J.J., Zhang, G.H., Wang, Y.H., et al: ‘A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation’, Transp. Res. C, Emerg., 2015, 51, pp. 2940.
    20. 20)
      • 20. Li, L., Li, Y.B., Li, Z.H.: ‘Efficient missing data imputing for traffic flow by considering temporal and spatial dependence’, Transp. Res. C, Emerg., 2013, 34, pp. 108120.
    21. 21)
      • 21. Li, L., He, S., Zhang, J., et al: ‘Short-term highway traffic flow prediction based on a hybrid strategy considering temporal-spatial information’, J. Adv. Transp., 2016, 50, (8), pp. 20292040.
    22. 22)
      • 22. Li, L., Fratrović, T., Zhang, J., et al: ‘Traffic speed prediction for highway operations based on a symbolic regression algorithm’, PROMET, 2017, 29, (4), pp. 433441.
    23. 23)
      • 23. Zhong, M., Lingras, P., Sharma, S.: ‘Estimation of missing traffic counts using factor, genetic, neural, and regression techniques’, Transp. Res. C, Emerg., 2004, 12, (2), pp. 139166.
    24. 24)
      • 24. Chang, H., Park, D., Lee, Y., et al: ‘Multiple time period imputation technique for multiple missing traffic variables: nonparametric regression approach’, Can. J. Civil Eng., 2012, 39, (4), pp. 448459.
    25. 25)
      • 25. Asif, M.T., Mitrovic, N., Dauwels, J., et al: ‘Matrix and tensor based methods for missing data estimation in large traffic networks’, IEEE Trans. Intell. Transp., 2016, 17, (7), pp. 18161825.
    26. 26)
      • 26. Cheng, A., Jiang, X., Li, Y., et al: ‘Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method’, Physica A, 2017, 466, pp. 422434.
    27. 27)
      • 27. Li, Y., Jiang, X., Zhu, H., et al: ‘Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory’, Nonlinear Dyn., 2016, 85, (1), pp. 179194.
    28. 28)
      • 28. Ho, S.L., Xie, M., Goh, T.N.: ‘A comparative study of neural network and Box–Jenkins ARIMA modeling in time series prediction’, Comput. Ind. Eng., 2002, 42, (2-4), pp. 371375.
    29. 29)
      • 29. Chang, H., Lee, Y., Yoon, B., et al: ‘Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences’, IET Intell. Transp. Syst., 2012, 6, (3), pp. 292305.
    30. 30)
      • 30. Moahmed, T.A., El Gayar, N., Atiya, A.F.: ‘Forward and backward forecasting ensembles for the estimation of time series missing data’. IAPR Workshop on Artificial Neural Networks in Pattern Recognition, 2014.
    31. 31)
      • 31. ‘Performance Measurement System, PeMS’, University of California, Berkeley’, Available at http://Pems.Dot.Ca.Gov/, accessed 20 April 2013.
    32. 32)
      • 32. Tak, S., Woo, S., Yeo, H.: ‘Data-driven imputation method for traffic data in sectional units of road links’, IEEE Trans. Intell. Transp., 2016, 17, (6), pp. 17621771.

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