Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

Applied Stochastic Models in Business and Industry

Volumn 28, Issue 4, 2012, Pages 297-315

  Kamarianakis, Yiannis ^a   Shen, Wei ^b   Wynter, Laura ^a  

^a IBM RESEARCH   (United States)

^b Walmart ECommerce   (United States)

Author keywords

adaptive LASSO; real time predictions; threshold regressions; traffic forecasting

Indexed keywords

ADAPTIVE LASSO; AVERAGE SPEED; HIGHWAY NETWORKS; LEAST ABSOLUTE DEVIATIONS; LINEAR REGIME; MEASUREMENT LOCATIONS; MODEL SELECTION; NON-LINEARITY; PENALIZED ESTIMATION; REAL TIME TRAFFICS; REAL-TIME PREDICTION; REGIME SWITCHING; ROAD TRAFFIC; SELECTION OPERATORS; SPACE-TIME MODEL; THRESHOLD REGRESSIONS; TRAFFIC DYNAMICS; TRAFFIC FORECASTING; TRAFFIC VARIABLES; TRANSPORTATION TECHNOLOGY; URBAN NETWORKS;

COMPUTER SIMULATION; STOCHASTIC MODELS;

ESTIMATION;

EID: 84865143932     PISSN: 15241904     EISSN: 15264025     Source Type: Journal    
DOI: 10.1002/asmb.1937     Document Type: Article

References (53)

1. Kamarianakis Y, Gao HO, Prastacos P,. Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions. Transportation Research Part C 2010; 18: 821-840.
2. Karlaftis MG, Vlahogianni EI,. Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transportation Research Part C 2011; 19: 387-399.
3. Vlahogianni EI, Golias JC, Karlaftis MG,. Short-term traffic forecasting: overview of objectives and methods. Transport Reviews 2006; 24: 533-558.
4. Vlahogianni EI, Karlaftis MG, Golias JC,. Temporal evolution of of short-term urban traffic flow: a nonlinear dynamics approach. Computer Aided Civil and Infrastructure Engineering 2008; 23: 536-548.
5. Yao ZS, Shao CF, Xiong ZH,. A study on short-term traffic flow forecasting based on a nonlinear time-series model. China Civil Engineering Journal 2007; 41: 104-109.
6. Min W, Wynter L,. Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C 2011; 19: 606-616.
7. Cryer JD, Chan K,. Time Series Analysis, 2nd ed. Springer: New York, USA, 2008.
8. Hansen BE,. Sample splitting and threshold estimation. Econometrica 2000; 68: 575-603.
9. Chan WS, Ng MW,. Robustness of alternative non-linearity tests for SETAR models. Journal of Forecasting 2004; 23: 215-231.
10. Tsay RS,. Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association 1989; 84: 231-240.
11. Kamarianakis Y, Prastacos P,. Space-time modeling of traffic flow. Computers & Geosciences 2005; 31: 119-133. (Pubitemid 40133572)
12. Min X, Hu J, Zhang Z,. Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model. IEEE Conference on Intelligent Transportation Systems, Madeira Island, Portugal, 2010; 1535-1540.
13. Huang H, Hsu N, Theobald D, Breidt FJ,. Spatial LASSO with applications to GIS model selection. Journal of Computational and Graphical Statistics 2010; 19: 963-983.
14. Gao Y, Sun S, Shi D,. Network-scale traffic modeling and forecasting with graphical LASSO. Advances in Neural Networks-ISSN 2011, Lecture Notes in Computer Science; 6676: 151-158.
15. Hofleitner A, El Ghaoui L, Bayen A,. Online least-squares estimation of time varying systems with sparse temporal evolution and application to traffic estimation. 50th IEEE Conference on Decision and Control, Orlando, Florida, 2011; 2595-2601.
16. Wan K, Kornhauser AL,. Link-data-based approximation of path travel time distribution with Gaussian copula estimated through LASSO. Transportation Research Board Annual Meeting, Washington, DC 20001 USA, 2010; 25 (1-25). Paper #10-2769.
17. Dielman TE,. A comparison of forecasts from least absolute value and least squares regression. Journal of Forecasting 1986; 5: 189-195.
18. Koopman SJ, Ooms M,. Forecasting economic time series using the unobserved components time series models. The Oxford Handbook of Economic Forecasting, Clements, and, Hendry, (eds). Oxford University Press: New York, USA, 2011; 124-162.
19. Williams BM, Hoel LA,. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. Journal of Transportation Engineering 2003; 129: 664-672. (Pubitemid 37470989)
20. 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: 512-522.
21. Boto-Giralda D, Diaz-Pernas FJ, Gonzalez-Ortega D, Diez-Higuera FJ, Anton-Rodriguez M, Martinez-Zarzuela M, Torre-Diez I,. Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks. Computer-Aided Civil and Infrastructure Engineering 2010; 25: 530-545.
22. van Dijk D, Teräsvirta T, Franses PH,. Smooth transition autoregressive models-a survey of recent developments. Econometric Reviews 2002; 21: 1-47.
23. Franses PH, van Dijk D,. Nonlinear Time Series Models in Empirical Finance. Cambridge University Press: Cambridge, UK, 2000.
24. Kamarianakis Y, Prastacos P,. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transportation Research Record-Journal of the Transportation Research Board 2003; 1857: 74-84.
25. Lin S, Huang H, Zhu D, Wang T,. The application of space-time ARIMA model on traffic flow forecasting. IEEE Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, China, 2009; 3408-3412.
26. Lee TH, White H, Granger CWJ,. Testing for neglected nonlinearity in time series models. Journal of Econometrics 1993; 56: 269-290.
27. Strikholm B, Teräsvirta T,. A sequential procedure for determining the number of regimes in a threshold autoregressive model. Econometrics Journal 2006; 9: 472-491. (Pubitemid 44651005)
28. Andrews DWK, Ploberger W,. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 1994; 62: 1383-1414.
29. Chan KS,. Percentage points of likelihood ratio tests for threshold autoregression. Journal of Royal Statistical Society Series B 1990; 3: 691-696.
30. Teräsvirta T, Lin CF, Granger CWJ,. Power of the neural network linearity test. Journal of Time Series Analysis 1993; 14: 209-220.
31. Gonzalo J, Pitarakis J,. Estimation and model selection-based inference in single and multiple threshold models. Journal of Econometrics 2002; 110: 319-352.
32. Teräsvirta T,. Forecasting economic variables with nonlinear models. Handbook of Economic Forecasting 2006; 1: 413-457.
33. Bai J, Perron P,. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 2003; 18: 1-22.
34. Tsay RS,. Testing and modeling multivariate threshold models. Journal of the American Statistical Association 1998; 93: 1188-1202.
35. Cheng T, Haworth J, Wang J,. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems 2011. DOI: 10.1007/s10109-011- 0149-5.
36. Borovkova SA, Lopuhaä HP, Ruchjana BN,. Consistency and asymptotic normality of least squares estimators in generalized space-time models. Statistica Neerlandica 2008; 62: 482-508.
37. Krolzig HM, Hendry DF,. Computer automation of general-to-specific model selection procedures. Journal of Economic Dynamics and Control 2001; 25: 831-866. (Pubitemid 33632171)
38. Tibshirani R,. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B 1996; 58: 267-288.
39. Zhao P, Yu B,. On model selection consistency of LASSO. Journal of Machine Learning Research 2006; 7: 2541-2563. (Pubitemid 44866738)
40. Efron B, Hastie T, Johnstone I, Tibshirani R,. Least angle regression. Annals of Statistics 2004; 32: 407-499. (Pubitemid 41250302)
41. Zou H,. The adaptive LASSO and its oracle properties. Journal of the American Statistical Association 2006; 101: 1418-1429.
42. Wang H, Li G, Jiang G,. Robust regression shrinkage and consistent variable selection through the LAD-LASSO. Journal of Business and Economic Statistics 2007; 25: 347-355.
43. Liu Z, Sharma S, Datla S,. Imputation of missing traffic data during holiday periods. Transportation Planning and Technology 2008; 5: 525-544.
44. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB,. Missing value estimation methods for DNA microarrays. Bioinformatics 2001; 17: 520-525. (Pubitemid 32600498)
45. Gardner ES,. Exponential smoothing: the state of the art. Journal of Forecasting 1985; 4: 128.
46. Kamarianakis Y, Kanas A, Prastacos P,. Modeling traffic flow volatility dynamics in an urban network. Transportation Research Record-Journal of the Transportation Research Board 2005; 1923: 18-27.
47. Hyndman RJ, Koehler AB,. Another look at measures of forecast accuracy. International Journal of Forecasting 2006; 22: 679-688. (Pubitemid 44524717)
48. Kamarianakis Y, Gao HO, Holmén B, Sonntag D,. Robust modeling and forecasting of diesel particle number emissions rates. Transportation Research Part D 2011; 16: 435-443.
49. Zhong M, Sharma S,. Matching hourly, daily, and monthly traffic patterns to estimate missing volume data. Transportation Research Record: Journal of the Transportation Research Board 2006; 1957: 32-42. (Pubitemid 44877649)
50. Pesaran MH, Pick A, Timmermann AG,. Variable selection and inference for multi-period forecasting problems. CEPR Discussion Paper No. DP7139, 2009.
51. Bickel PJ, Chen C, Kwon J, Rice J, van Zwet E, Varaiya P,. Measuring traffic. Statistical Science 2007; 22: 581-597.
52. Ou Q, van Lint H, Hoogendoorn SP,. Fusing heterogeneous and unreliable data from traffic sensors. Interactive Collaborative Information Systems, Vol. SCI 281, Babuska R, Groen FCA, (eds). Springer-Verlag: Berlin Heidelberg, 2010; 511-545.
53. van Lint JWC, Hoogendoorn SP,. A robust and efficient method for fusing heterogeneous data from traffic sensors on freeways. Computer-Aided Civil and Infrastructure Engineering 2010; 25: 596-612.