A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

Transportation Research Part C: Emerging Technologies

Volumn 19, Issue 6, 2011, Pages 1306-1318

  Fei, Xiang ^a   Lu, Chung Cheng ^b   Liu, Ke ^c  

^a IBM CHINA RESEARCH LAB   (China)

^b NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^c IBM Research   (China)

Author keywords

Adaptive control; Advanced Traveler Information Systems; Bayesian inference; Prediction confidence intervals; Real time travel time prediction

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; BAYESIAN NETWORKS; FORECASTING; INFERENCE ENGINES; LAGRANGE MULTIPLIERS; REAL TIME SYSTEMS; TIME VARYING CONTROL SYSTEMS; TRAFFIC CONGESTION; TRAVEL TIME;

ADAPTIVE CONTROL; BAYESIAN FORECASTING; BAYESIAN INFERENCE; DYNAMIC LINEAR MODEL; PREDICTION CONFIDENCE; REAL TIME TRAVEL; TRAVEL TIME DISTRIBUTIONS; TRAVEL TIME PREDICTION;

ADVANCED TRAVELER INFORMATION SYSTEMS;

ADVANCED TRAVELER INFORMATION SYSTEM; BAYESIAN ANALYSIS; CONFIDENCE INTERVAL; NUMERICAL MODEL; PREDICTION; TRAFFIC CONGESTION; TRAVEL TIME;

UNITED STATES; VIRGINIA;

EID: 80052718938     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2010.10.005     Document Type: Article

References (39)

1. Bajwa, S.I, Chung, E., Kuwahara, M., 2005. Performance evaluation of an adaptive travel time prediction model. In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, 13-16 September, 2005.
2. Cetin M., Comert G. Short-term traffic flow prediction with regime switching models. Transportation Research Record 2006, 1965:23-31.
3. Chen M., Chien S. Dynamic freeway travel-time prediction with probe vehicle data, link based versus path based. Transportation Research Record 2001, 1768:157-161.
4. Chien S., Kuchipudi C.M. Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering 2003, 129(6):608-616.
5. Chu, L.Y., Oh, J.S., Recker, W., 2005. Adaptive kalman filter based freeway travel time estimation. In: Paper Presented in the 84th TRB Annual Meeting, Washington, DC, January 2005.
6. Fei, X., 2000. Shanghai Railway Bureau Freight Forecasting Model and Management Information System Development. Master Thesis, Shanghai Tongji University, Shanghai, China.
7. Federal Highway Administration, 1998. Travel Time Data Collection Handbook. Report FHWA-PL-98-035, Office of Highway Information Management, Federal Highway Administration, US Department of Transportation.
8. Juri N.R., Unnikrishnan A., Waller S.T. Integrated traffic simulation-statistical analysis framework for online prediction of freeway travel time. Transportation Research Record 2007, 2039:24-31.
9. Karlaftisand M.G., Vlahogianni E.I. Memory properties and fractional integration in transportation time-series. Transportation Research Part C 2009, 17(4):444-453.
10. Kuchipudi C.M., Chien S. Development of a hybrid model for dynamic travel-time prediction. Transportation Research Record 2003, 1855:22-31.
11. Kwon J.Y., Petty K. Travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes. Transportation Research Record 2005, 1935:147-153.
12. Kwon J.Y., Coifman B., Bickel P. Day-to-day travel-time trends and travel-time prediction from loop detector data. Transportation Research Record 2000, 1717:120-129.
13. Lam, S.H., Toan, T.D., 2008. Short-term travel time predictions using support vector regression. In: Paper Presented in the 87th TRB Annual Meeting, Washington, DC, January 2008.
14. Lan L.W., Sheu J.B., Huang Y.S. Investigation of temporal freeway traffic patterns in reconstructed state spaces. Transportation Research Part C 2008, 16(3):116-136.
15. Lee W.H., Tseng S.S., Tsai S.H. A knowledge based real-time travel time prediction system for urban network. Expert System with Applications 2009, 36:4239-4247.
16. Liu, K., Fei, X., 2006. Dynamic short-term traffic flow forecasting for congested freeway. In Proc. of the 13rd World Congress on Intelligent Transport System (CD-ROM) London, England, 2006.
17. Liu H., van Zuylen H., van Lint H., Salomons M. Predicting urban arterial travel time with state-space neural networks and Kalman filters. Transportation Research Record 2006, 1968:99-108.
18. Nanthawichit C., Nakatsuji T., Suzuki H. Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway. Transportation Research Record 2003, 2987:49-59.
19. Park T., Lee S. A Bayesian approach for estimating link travel time on urban arterial road network. Lecture Notes in Computer Science 2004, 3043:1017-1025.
20. Park D.J., Rilett L.R. Forecasting multiple-period freeway link travel times using modular neural networks. Transportation Research Record 1998, 1617:163-170.
21. Pourahmadi M. Foundations of Time Series Analysis and Prediction Theory 2001, A Wiley-Interscience Publication.
22. Rilett L.R., Park D.J. Direct forecasting of freeway corridor travel times using spectral basis neural networks. Transportation Research Record 2001, 1752:140-147.
23. Stathopoulos A., Karlaftis M.G. A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C 2003, 11(2):121-135.
24. Tsekeris T., Stathopoulos A. Real-time traffic volatility forecasting in urban arterial networks. Transportation Research Record 2006, 1964:146-156.
25. van Hinsbergen C.P., van Lint J.W.C. Bayesian combination of travel time prediction models. Transportation Research Record 2008, 2064:73-80.
26. van Hinsbergen, C.P., van Lint, J.W.C., Sanders, F.M., 2007. Short term traffic prediction models. In Proc. of the 14th World Congress on Intelligent Transport System (CD-ROM) Beijing, China, 2007.
27. van Hinsbergen C.P., van Lint J.W.C., van Zuylen H.J. Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C 2009, 17(5):498-509.
28. van Lint, J.W.C., 2003. Confidence intervals for real-time freeway travel time prediction. In: Proceedings of the 2003 IEEE Conference on Intelligent Transportation Systems, Shanghai, China, IEEE.
29. van Lint J.W.C. Online learning solutions for freeway travel time prediction. IEEEE Transactions on Intelligent Transportation Systems 2008, 9(1):38-47.
30. van Lint J.W.C., Hoogendoorn S.P., Van Zuylen H.J. Freeway travel time prediction with state-space neural networks-modeling state-space dynamics with recurrent neural networks. Transportation Research Record 2002, 1811:30-39.
31. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer.
32. Vlahogianni E.I., Karlaftis M.G., Golias J.C. Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume. Transportation Research Part C 2006, 14(2):351-367.
33. Wang Y., Papageorgiou M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transportation Research Part B 2005, 39(2):141-167.
34. Wang Y., Papageorgiou M., Messmer A. Renaissance - a unified macroscopic model based approach to real-time freeway network traffic surveillance. Transportation Research Part C 2006, 14(3):190-212.
35. West M., Harrison J. Bayesian Forecasting and Dynamic Models 1999, Springer Series in Statistics. second ed.
36. Williams B.M., Hoel L.A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. ASCE Journal of Transportation Engineering 2003, 129(6):664-672.
37. Yang F., Yin Z.Z., Liu H., Ran B. Online recursive algorithm for short-term traffic prediction. Transportation Research Record 2004, 1879:1-8.
38. Zhang X.Y., Rice J.A. Short-term travel time prediction using a time-varying coefficient linear model. Transportation Research Part C 2003, 11(8):187-210.
39. Zhou X., Mahmassani H.S. A structural state space model for real-time origin-destination demand estimation and prediction in a day-to-day updating framework. Transportation Research Part B 2007, 41(8):823-840.