Missing traffic data: Comparison of imputation methods

IET Intelligent Transport Systems

Volumn 8, Issue 1, 2014, Pages 51-57

  Li, Yuebiao ^a   Li, Zhiheng ^a   Li, Li ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMPUTATION METHODS; INTERPOLATION METHOD; MISSING DATA IMPUTATIONS; PROBABILISTIC PRINCIPAL COMPONENT ANALYSIS; RECONSTRUCTION ERROR; STATISTICAL LEARNING METHODS; TRAFFIC MANAGEMENT AND CONTROLS; TRAFFIC PREDICTION;

MECHANICAL ENGINEERING; TRANSPORTATION;

TRAFFIC CONTROL;

EID: 84893144148     PISSN: 1751956X     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-its.2013.0052     Document Type: Article

References (30)

1. Turner, S., Albert, L., Gajewski, B., Eisele, W.: 'Archived intelligent transportation system data quality: preliminary analyses of San Antonio TransGuide data', Transp. Res. Rec., 2000, 1719, (1), pp. 77-84
2. Chen, C., Kwon, J., Rice, J., Skabardonis, A., Varaiya, P.: 'Detecting errors and imputing missing data for single loop surveillance systems', Transp. Res. Rec., 2002, 1855, (1), pp. 160-167
3. 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., 2009, 10, (3), pp. 512-522
4. Performance Measurement System, PeMS, University of California, Berkeley. Available at http://pems.dot.ca.gov/, accessed April 2013
5. Ahmed, M.S., Cook, A.R.: 'Analysis of freeway traffic time-series data by using Box-Jenkins techniques', Transp. Res. Rec., 1979, (722), pp. 1-9
6. Lee, S., Fambro, D.: 'Application of the subset ARIMA model for short-term freeway traffic volume forecasting', Transp. Res. Rec., 1999, 1678, (1), pp. 179-188
7. Zhong, M., Sharma, S., Lingras, P.: 'Genetically designed models for accurate imputations of missing traffic counts', Transp. Res. Rec., 2004, 1879, (1), pp. 71-79
8. Zhang, C.S., Sun, S., Yu, G.: 'A Bayesian network approach to time series forecasting of short-term traffic flows'. Proc. IEEE Conf. Intelligent Transportation Systems, Washington, D.C., 2004, pp. 216-221
9. Ghosh, B., Basu, B., O'Mahony, M.: 'Bayesian time-series model for short-term traffic flow forecasting', ASCE J. Transp. Eng., 2007, 133, (3), pp. 180-189
10. Dia, H.: 'An object-oriented neural network approach to short-term traffic forecasting', Eur. J. Oper. Res., 2001, 131, (2), pp. 253-261
11. Dougherty, M.: 'A review of neural networks applied to transport', Transp. Res. C, Emerg. Technol., 1995, 3, (4), pp. 247-260
12. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: 'Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach', Transp. Res. C, Emerg. Technol., 2005, 13, (3), pp. 211-234
13. Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., Han, L.D.: 'Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions', Expert Syst. Appl., 2009, 36, (3), pp. 6164-6173
14. Jin, X., Zhang, Y., Yao, D.: 'Simultaneously prediction of network traffic flow based on PCA-SVR'. Lect. Notes Comput. Sci., 2007, 4492, pp. 1022-1031
15. Chen, C., Wang, Y., Li, L., Hu, J., Zhang, Z.: 'The retrieval of intra-day trend and its influence on traffic prediction', Transp. Res. C, Emerg. Technol., 2012, 22, pp. 103-118
16. Yin, W., Tuite, P.M., Rakha, H.: 'Imputing erroneous data of single-station loop detectors for nonincident conditions: comparison between temporal and spatial methods', J. Intell. Transp. Syst., Technol. Plan. Oper., 2012, 16, (3), pp. 159-176
17. Zhong, M., Sharma, S., Liu, Z.: 'Assessing robustness of imputation models based on data from different jurisdictions: examples of Alberta and Saskatchewan, Canada', Transp. Res. Rec., 2005, 1917, (1), pp. 116-126
18. Troyanskaya, O., Cantor, M., Sherlock, G., et al.: 'Missing value estimation methods for DNA microarrays', Bioinformatics, 2001, 17, (6), pp. 520-525
19. Kim, H., Golub, G.H., Park, H.: 'Missing value estimation methods for DNA microarrays gene expression data: local least squares imputation', Bioinformatics, 2005, 21, (2), pp. 187-198
20. Ni, D., Leonard II, J.D.: 'Markov chain Monte Carlo multiple imputation using Bayesian networks for incomplete intelligent transportation systems data', Transp. Res. Rec., 2005, 1935, (1), pp. 57-67
21. Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: 'Markov chain Monte Carlo in practice' (Chapman & Hall, London, 1996)
22. Tipping, M.E., Bishop, C.M.: 'Mixtures of probabilistic principal component analyzers', Neural Comput., 1999, 11, (2), pp. 443-482
23. Brockwell, P.J., Davis, R.A.: 'Introduction to time series and forecasting' (Springer-Verlag, New York, USA, 2002, 2nd edn.)
24. Ueda, N., Nakano, R., Ghahramani, Z., Hinton, G.E.: 'Split and merge EM algorithm for improving Gaussian mixture density estimates'. Proc. IEEE Workshop on Neural Networks for Signal Processing, 1998, pp. 274-283
25. Pearson, K.P.: 'Contributions to the mathematical theory of evolution', Philos. Trans.R. Soc. A, 1894, 185, pp. 71-110
26. Tanner, M.A., Wong, W.H.: 'The calculation of posterior distributions by data augmentation', J. Am. Stat. Assoc., 1987, 82, (398), pp. 528-540
27. Dempster, A.P., Laird, N.M., Rubin, D.B.: 'Maximum likelihood from incomplete data via the EM algorithm', J. R. Stat. Soc. B, 1977, 39, (1), pp. 1-38
28. McLachlan, G., Krishnan, T.: 'The EM algorithm and extensions' (John Wiley & Sons, 2008, 2nd edn.)
29. Liu, Z., Sharma, S., Datla, S.: 'Imputation of missing traffic data during holiday periods', Transp. Plan. Technol., 2008, 31, (5), pp. 525-544
30. http://www.wunderground.com/history, wunderground.com, accessed June 2012