Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

Transportation Research Part C: Emerging Technologies

Volumn 34, Issue , 2013, Pages 108-120

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

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Kernel probabilistic principle component analysis (KPPCA); Missing data; Probabilistic principle component analysis (PPCA); Temporal and spatial dependence; Traffic flow

Indexed keywords

DATA FUSION;

MISSING DATA; MISSING DATA PROBLEM; POTENTIAL BENEFITS; PROBABILISTIC PRINCIPLE COMPONENT ANALYSIS; TEMPORAL AND SPATIAL; TRAFFIC FLOW; TRAFFIC FLOW INFORMATION; TRAFFIC FLOW PREDICTION;

PATTERN RECOGNITION;

ALGORITHM; COMPARATIVE STUDY; DATA PROCESSING; ERROR ANALYSIS; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; SPATIOTEMPORAL ANALYSIS; TRAFFIC MANAGEMENT;

EID: 84880340417     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2013.05.008     Document Type: Article

References (51)

1. Ahmed M.S., Cook A.R. Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transportation Research Record 1979, 722:1-9.
2. Bickel P.J., Chen C., Kwon J., Rice J., van Zwet E., Varaiya P. Measuring traffic. Statistical Science 2007, 22(4):587-597.
3. Boyd S., Vandenberghe L. Convex Optimization 2004, Cambridge University Press.
4. Chen C., Kwon J., Rice J., Skabardonis A., Varaiya P. Detecting errors and imputing missing data for single loop surveillance systems. Transportation Research Record 2002, 1855:160-167.
5. Chen C., Wang Y., Li L., Hu J., Zhang Z. The retrieval of intra-day trend and its influence on traffic prediction. Transportation Research Part C: Emerging Technologies 2012, 22:103-118.
6. De Boor C. A Practical Guide to Splines 1978, Springer-Verlag.
7. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 1977, 39(1):1-38.
8. Der Voort M., Dougherty M., Watson S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies 1996, 4(5):307-318.
9. Haworth J., Cheng T. Non-parametric regression for space-time forecasting under missing data. Computers, Environment and Urban Systems 2012, 36(6):538-550.
10. Hoyle D.C. Automatic PCA dimension selection for high dimensional data and small sample sizes. Journal of Machine Learning Research 2008, 9:2733-2759.
11. Ilin A., Raiko T. Practical approaches to principal component analysis in the presence of missing values. Journal of Machine Learning Research 2010, 11:1957-2000.
12. Jin X., Zhang Y., Li L., Hu J. Robust PCA based abnormal traffic flow pattern isolation and loop detector fault detection. Tsinghua Science and Technology 2008, 13(6):829-835.
13. Kamarianakis Y., Prastacos P. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transportation Research Record 2003, 1857:74-84.
14. Kamarianakis Y., Shen W., Wynter L. Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO. Applied Stochastic Models in Business and Industry 2012, 28(4):297-315.
15. Karlaftis M.G. Discussion. Applied Stochastic Models in Business and Industry 2012, 28(4):316-318.
16. Karlaftis M.G., Vlahogianni E.I. Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transportation Research Part C: Emerging Technologies 2011, 19(3):387-399.
17. Lawrence N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research 2005, 6:1783-1816.
18. Li, Y., Li, Z., Li, L., Zhang, Y., 2013. Comparison on PPCA, KPPCA and MPPCA based missing data imputing for traffic flow. In: Proceedings of International Conference on Transportation Information and Safety (in press).
19. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data 1987, John Wiley & Sons Press.
20. McLachlan G., Krishnan T. The EM Algorithm and Extensions 2008, Wiley, Hoboken, NJ, USA. second ed.
21. Min W., Wynter L. Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C: Emerging Technologies 2011, 19(4):606-616.
22. Min, X., Hu, J., Zhang, Z., 2010. Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model. In: Proceedings of IEEE Conference on Intelligent Transportation Systems, pp. 1535-1540.
23. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993, 6(4):525-533.
24. Ni D., Leonard J.D. Markov Chain Monte Carlo multiple imputation using Bayesian Networks for incomplete intelligent transportation systems data. Transportation Research Record 2005, 1935:57-67.
25. Ni D., Leonard J.D., Guin A., Feng C. Multiple imputation scheme for overcoming the missing values and variability issues in ITS data. ASCE Journal of Transportation Engineering 2005, 131(12):931-938.
26. PeMS, California Performance Measurement System. http://pems.eecs.berkeley.edu.
27. Qu L., Li L., Zhang Y., Hu J. PPCA-based missing data imputation for traffic flow volume: a systematical approach. IEEE Transactions on Intelligent Transportation Systems 2009, 10(3):512-522.
28. Ramezani A., Moshiri B., Abdulhai B., Kian A.R. Distributed maximum likelihood estimation for flow and speed density prediction in distributed traffic detectors with Gaussian mixture model assumption. IET Intelligent Transport Systems 2010, 6(2):215-222.
29. Roweis S., Ghahramani Z. A unifying review of linear Gaussian models. Neural Computation 1999, 11(2):305-345.
30. Sanguinetti G., Lawrence N.D. Missing data in kernel PCA. Lecture Notes in Computer Science 2006, 4212:751-758.
31. Schölkopf B., Smola A., Müller K.-R. Kernel principal component analysis. Lecture Notes in Computer Science 1997, 1327:583-588.
32. Sharma S., Lingras P., Zhong M. Effect of missing values estimations on traffic parameters. Transportation Planning and Technology 2004, 27(2):119-144.
33. Shawe-Taylor, Cristianini J.N. Kernel Methods for Pattern Analysis 2004, Cambridge University Press.
34. Smith B.L., Scherer W.T., Conklin J.H. Exploring imputation techniques for missing data in transportation management systems. Transportation Research Record 2003, 1836:132-142.
35. Stathopoulos A., Karlaftis M.G. A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C: Emerging Technologies 2003, 11(2):121-135.
36. Stathopoulos A., Tsekeris T. Methodology for processing archived ITS data for reliability analysis in urban networks. IEE Proceedings of Intelligent Transportation Systems 2006, 153(1):105-112.
37. Sun S., Xu M. Variational inference for infinite mixtures of Gaussian processes with applications to traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems 2011, 12(2):466-475.
38. Sun S., Zhang C., Yu G. A Bayesian network approach to traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 2006, 7(1):124-132.
39. Sun S., Huang R., Gao Y. Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks. Journal of Transportation Engineering 2012, 138(11):1358-1367.
40. Tan H., Feng G., Feng J., Wang W., Zhang Y.-J., Li F. A tensor-based method for missing traffic data completion. Transportation Research Part C: Emerging Technologies 2013, 28:15-27.
41. Tipping M.E., Bishop C.M. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 1999, 21(3):611-622.
42. Turner S., Albert L., Gajewski B., Eisele W. Archived intelligent transportation system data quality: preliminary analyses of San Antonio TransGuide data. Transportation Research Record 2000, 1719:77-84.
43. van Lint J.W.C., Hoogendoorn S.P., van Zuylen H.J. Accurate freeway travel time prediction with state-space neural networks under missing data. Transportation Research Part C: Emerging Technologies 2005, 13(5-6):347-369.
44. Vlahogianni E.I., Golias J.C., Karlaftis M.G. Short-term traffic forecasting: overview of objectives and methods. Transportation Reviews 2004, 24(5):533-557.
45. Vlahogianni E.I., Karlaftis M.G., Golias J.C. Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transportation Research Part C: Emerging Technologies 2005, 13(3):211-234.
46. Williams B.M. Multivariate vehicular traffic flow prediction evaluation of ARIMAX modeling. Transportation Research Record 2001, 1776:194-200.
47. Yin W., Murray-Tuite P., Rakha H. Imputing erroneous data of single-station Loop detectors for nonincident conditions: comparison between temporal and spatial methods. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 2012, 16(3):159-176.
48. Zhang J., Wang F.-Y., Wang K., Lin W.-H., Xu X., Chen C. Data-driven intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems 2011, 12(4):1624-1639.
49. Zhong M., Lingras P., Sharma S. Estimation of missing traffic counts using factor, genetic, neural, and regression techniques. Transportation Research Part C: Emerging Technologies 2004, 12(2):139-166.
50. Zhong M., Sharma S., Lingras P. Genetically designed models for accurate imputations of missing traffic counts. Transportation Research Record 2004, 1879:71-79.
51. Zhong M., Sharma S., Liu Z. Assessing robustness of imputation models based on data from different jurisdictions: examples of Alberta and Saskatchewan, Canada. Transportation Research Record 2005, 1917:116-126.