Short-term traffic state prediction based on temporal-spatial correlation

IEEE Transactions on Intelligent Transportation Systems

Volumn 14, Issue 3, 2013, Pages 1242-1254

  Pan, T L ^a   Sumalee, A ^a   Zhong, R X ^a,b   Indra Payoong, N ^c  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b SUN YAT SEN UNIVERSITY   (China)

^c BURAPHA UNIVERSITY   (Thailand)

Author keywords

Abnormal traffic conditions; rolling horizon; spatialoral correlation; stochastic cell transmission model (SCTM); traffic state prediction

Indexed keywords

CELL TRANSMISSION MODEL; DEMAND AND SUPPLY UNCERTAINTIES; MULTI-VARIATE NORMAL DISTRIBUTIONS; ROLLING HORIZON; SPATIAL CORRELATION ANALYSIS; TEMPORAL-SPATIAL CORRELATIONS; TRAFFIC CONDITIONS; TRAFFIC STATE;

DYNAMICAL SYSTEMS; FORECASTING; NORMAL DISTRIBUTION; NUMERICAL ANALYSIS; RANDOM PROCESSES; STOCHASTIC SYSTEMS;

STREET TRAFFIC CONTROL;

EID: 84883802662     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2013.2258916     Document Type: Article

References (35)

1. C. Chan, T. Dillon, J. Singh, and E. Chang, Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm, IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 644-654, Jun. 2012.
2. K. Chan, S. Khadem, T. Dillon, V. Palade, J. Singh, and E. Chang, Selection of significant on-road sensor data for short-term traffic flow forecasting using the Taguchi method, IEEE Trans. Ind. Informat., vol. 8, no. 2, pp. 255-266, May 2012.
3. K. Chan, T. Dillon, and E. Chang, A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems, IEEE Trans. Ind. Electron., vol. 60, no. 10, pp. 4714-4725, Oct. 2013.
4. K. Chan, T. Dillon, V. Palade, E. Chang, and J. Singh, Prediction of shortterm traffic variables using intelligent swarm-based neural networks, IEEE Trans. Control Syst. Technol., vol. 21, no. 1, pp. 263-274, Jan. 2013.
5. S. Chandra and H. Al-Deek, Predictions of freeway traffic speeds and volumes using vector autoregressive models, J. Intell. Transp. Syst., vol. 13, no. 2, pp. 53-72, Apr. 2009.
6. C. Chen, J. Kwon, J. Rice, A. Skabardonis, and P. Varaiya, Detecting errors and imputing missing data for single loop surveillance systems, Transp. Res. Rec., vol. 1855, pp. 160-167, 2003.
7. C. Chui and G. Chen, Kalman Filtering With Real-Time Applications, 4th ed. Berlin, Germany: Springer-Verlag, 2009.
8. S. Clark, M. Dougherty, and H. Kirby, The use of neural networks and time series models for short-term traffic forecasting: A comparative study, in Proc. PTRC 21st Annu. Meet.-Transportation Planning Methods, Manchester, U.K., 1993, pp. 151-162.
9. H. Jula, M. Dessouky, and P. Ioannou, Real-time estimation of travel times along the arcs and arrival times at the nodes of dynamic stochastic networks, IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 97-110, Mar. 2008.
10. M. Karlaftis and E. Vlahogianni, Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transp. Res. Part C, vol. 19, no. 3, pp. 387-399, Jun. 2011.
11. J. Kwon, P. Varaiya, and A. Skabardonis, Estimation of truck traffic volume from single loop detectors with lane-to-lane speed correlation, Transp. Res. Rec., no. 1856, pp. 106-117, 2003.
12. J. Long, Z. Gao, P. Orenstein, and H. Ren, Control strategies for dispersing incident-based traffic jams in two-way grid networks, IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 469-481, Jun. 2012.
13. J. McCrea and S. Moutari, A hybrid macroscopic-based model for traffic flow in road networks, Eur. J. Oper. Res., vol. 207, no. 2, pp. 676-684, Dec. 2010.
14. L. Muñoz, X. Sun, R. Horowitz, and L. Alvarez, A piecewise-linearized cell transmission model and parameter calibration methodology, Transp. Res. Rec., no. 1965, pp. 183-191, 2006. (Pubitemid 46279050)
15. W. Min and L. Wynter, Real-time road traffic prediction with spatiotemporal correlations, Transp. Res. Part C, vol. 19, no. 4, pp. 606-616, Aug. 2011.
16. T. Pan, The stochastic dynamic journey time reliability analysis by considering the spatial and temporal correlations, M.Phil. dissertation, Dept. Civil Environmental Eng. The Hong Kong Polytechnic Univ., Hong Kong, 2012.
17. S. Peeta and A. Ziliaskopoulos, Foundations of dynamic traffic assignment: The past, the present, and the future, Netw. Spatial Econom., vol. 1, no. 3/4, pp. 233-265, Sep. 2001.
18. M. Schönhof and D. Helbing, Empirical features of congested traffic states and their implications for traffic modeling, Transp. Sci., vol. 41, no. 2, pp. 135-166, May 2007.
19. J. Sheu, L. Lan, and Y. Huang, Short-term prediction of traffic dynamics with real-time recurrent learning algorithms, Transportmetrica, vol. 5, no. 1, pp. 59-83, Jan. 2009.
20. A. Sumalee, R. Zhong, T. Pan, and W. Szeto, Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment, Transp. Res. Part B, vol. 45, no. 3, pp. 507-533, Mar. 2011.
21. A. Sumalee, T. Pan, R. Zhong, N. Uno, and N. Indra-Payoong, Dynamic stochastic journey time estimation and reliability analysis using stochastic cell transmission model: Algorithm and case studies, Transp. Res. Part C. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0968090X12001362, to be published.
22. W. Y. Szeto, B. Ghosh, B. Basu, and M. Mahony, Multivariate traffic forecasting technique using cell transmission model and SARIMA model, J. Transp. Eng., vol. 135, no. 9, pp. 658-667, Sep. 2009.
23. M. Tam and W. H. K. Lam, Using automatic vehicle identification data for travel time estimation in Hong Kong, Transportmetrica, vol. 4, no. 3, pp. 179-194, Sep. 2008.
24. T. Tchrakian, B. Basu, and M. O?Mahony, Real-time traffic flow forecasting using spectral analysis, IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 519-526, Jun. 2012.
25. L. Tsirigotis, E. Vlahogianni, and M. Karlaftis, Does information on weather affect the performance of short-term traffic forecasting models? Int. J. Intell. Transp. Syst. Res., vol. 10, no. 1, pp. 1-10, Jan. 2012.
26. Y. Tong, The Multivariate Normal Distribution. New York, NY, USA: Spring-Verlag, 1990.
27. E. Vlahogianni, J. Golias, and M. Karlaftis, Short-term traffic forecasting: Overview of objectives and methods, Transp. Rev., vol. 24, no. 5, pp. 533-557, Sep. 2004.
28. E. Vlahogianni and M. Karlaftis, Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures, Nonlinear Dyn., vol. 69, no. 4, pp. 1949-1963, Sep. 2012.
29. Y. Wang and M. Papageorgiou, Real-time freeway traffic state estimation based on extended Kalman filter: A general approach, Transp. Res. Part B, vol. 39, no. 2, pp. 141-167, Feb. 2005.
30. Y. Wang, P. Coppola, A. Messmer, A. Tzimitsi, M. Papageorgiou, and A. Nuzzolo, Real-time freeway network traffic surveillance: Large-scale field testing in Southern Italy, IEEE Trans. Intell. Transp. Syst., vol. 12, no. 2, pp. 548-562, Jun. 2011.
31. J. Yuan and K. Mills, A cross-correlation-based method for spatialoral traffic analysis, Perform. Eval., vol. 61, no. 2/3, pp. 163-180, Jul. 2005.
32. R. Zhong, A. Sumalee, T. Pan, and W. H. K. Lam, Stochastic cell transmission model for traffic network with demand and supply uncertainties, Transportmetrica, 2011. [Online]. Available: http://www.sciencedirect. com/science/article/pii/S0968090X12001362, to be published.
33. R. Zhong, T. Pan, A. Sumalee, and W. H. K. Lam, Traffic state estimation under abnormal traffic conditions using sum-of-norms regularization, Working paper.
34. R. Zhong, A. Sumalee, T. Pan, and W. H. K. Lam, A cell transmission model with lane changing and incorporation of stochastic demand and supply uncertainties for freeway traffic state estimation, in Proc. 4th Int. Symp. Dyn. Traffic Assign., Marthas Vineyard, MA, USA, 2012.
35. R. Zhong, A. Sumalee, T. Pan, and W. H. K. Lam, Optimal and robust strategies for freeway traffic management under demand and supply uncertainties: An overview and general theory, Transportmetrica, to be published.