Flow rate and time mean speed predictions for the urban freeway network using state space models

Transportation Research Part C: Emerging Technologies

Volumn 43, Issue , 2014, Pages 20-32

  Dong, Chunjiao ^a   Shao, Chunfu ^b   Richards, Stephen H ^a   Han, Lee D ^c  

^a UNIVERSITY OF TENNESSEE   (United States)

^b BEIJING JIAOTONG UNIVERSITY   (China)

^c University of Tennessee Knoxville   (United States)

Author keywords

Congested and non congested traffic; Short term prediction; Spatial temporal pattern; State space model; Traffic flow

Indexed keywords

AUTOREGRESSIVE MOVING AVERAGE MODEL; FLOW RATE; FORECASTING; SPEED; STATE SPACE METHODS;

CONGESTED TRAFFIC; SHORT TERM PREDICTION; SPATIAL TEMPORALS; STATE - SPACE MODELS; TRAFFIC FLOW;

TRAFFIC CONGESTION;

MOTORWAY; NUMERICAL MODEL; PREDICTION; SPATIAL ANALYSIS; TEMPORAL ANALYSIS; TIME SERIES; TRAFFIC CONGESTION; TRAFFIC MANAGEMENT; URBAN TRANSPORT;

BEIJING [CHINA]; CHINA;

EID: 84902532605     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2014.02.014     Document Type: Article

References (37)

1. Abdulhai B., Porwal H., Recker W. Short-term traffic flow prediction using neuro-genetic algorithms. ITS J. 2002, 7(1):3-41.
2. Ahmed S.A., Cook A.R. Analysis of freeway traffic time-series data using box-Jenkin techniques. Transport. Res. Rec. 1979, 722:1-9.
3. Benediktsson J.A., Swain P.H., Ersoy O.K. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geosci. Remote Sens. 1990, 28(4):540-552.
4. Davis G.A., Nihan N.L. Nonparametric regression and short term freeway traffic forecasting. J. Transport. Eng. 1991, 117(2):178-188.
5. Dong C., Shao C., Zhuge C., Meng M. The analysis of spatial and temporal characteristics for congested traffic on urban expressway. J. Beijing Univ. Technol. 2012, 38(8):1242-1246.
6. Dougherty M.S., Kirby H.C. The use of neural networks to recognize and predict traffic congestion. Traffic Eng. Control 1998, 34(6):311-314.
7. Drake J.S., Schofer J.L., May A.D. A statistical analysis of speed density hypotheses. Proc. Third Int. Symp. Theor. Traffic Flow 1967, 112-117.
8. Greenberg H. An analysis of traffic flow. Oper. Res. 1959, 7(1):79-85.
9. Hamed M.M., Al-Masaeid H.R., Said Z.M. Short-term prediction of traffic volume in urban arterials. J. Transport. Eng. 1995, 121(3):249-254.
10. Ishak S., Alecsandru C. Optimizing traffic prediction performance of neural networks under various topological, input and traffic condition settings. J. Transport. Eng. 2004, 130(4):452-465.
11. Kalman R.E. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960, 82(4):35-45.
12. Kaysi I., Ben-Akiva M., Koutsopoulos H. An integrated approach to vehicle routing and congestion prediction for real-time driver guidance. Transport. Res. Rec. 1993, 1408:66-74.
13. Lighthill M.J., Whitham G.B. On kinematic waves. II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. Lond. Ser., A Math. Phys. Sci. 1955, 229(1178):317-345.
14. May A.D. Traffic Flow Fundamentals 1990, Prentice-Hall Inc., New Jersey.
15. Okutani I., Stephanedes Y.J. Dynamic prediction of traffic volume through kalman filtering theory. Transport. Res. Part B: Methodol. 1984, 18(1):1-11.
16. Park B., Carroll J.M., Urbank T. Short-term freeway traffic volume forecasting using radial basis function neural network. Transport. Res. Rec. 1998, 1651:39-46.
17. Payne H.J. FREFLO: a macroscopic simulation model of freeway traffic. Transport. Res. Rec. 1979, 722:68-77.
18. Petridis V., Kehagias A., Petrou L., Bakirtzis A., Kiartzis S., Panagiotou H., Maslaris H. A Bayesian multiple models combination method for time series prediction. J. Intell. Rob. Syst. 2001, 31(1):69-89.
19. Richards P.I. Shock waves on the highway. Oper. Res. 1956, 4(1):42-51.
20. Smith B.L., Demetsky M.J. Short-term traffic flow prediction: neural network approach. Transport. Res. Rec. 1994, 1453:98-104.
21. Smith B.L., Oswald R.K. Meeting real-time requirements with imprecise computations: a case study in traffic flow forecasting. Comput. Aid. Civ. Infrastruct. Eng. 2003, 18(3):201-213.
22. Smith B.L., Williams B.M., Oswald K.R. Comparison of parametric and non-parametric models for traffic flow forecasting. Transport. Res. Part C: Emerg. Technol. 2002, 10(4):303-321.
23. Stathopoulos A., Karlaftis M.G. A multivariate state-space approach to urban traffic flow prediction. Transport. Res. Part C: Emerg. Technol. 2003, 11(2):121-135.
24. Tebaldi C., West M., Karr A.F. Statistical analysis of freeway traffic flows. J. Forecasting 2002, 21(1):39-68.
25. Tu J.V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 1996, 49(11):1225-1231.
26. Underwood R.T. Speed, Volume, and Density Relationships, Quality and Theory of Traffic Flow 1961, Yale Bureau of Highway Traffic, New Haven, Connecticut, pp. 141-188.
27. van Lint J.W.C., Hoogendoorn S.P., van Zuylen H.J. Freeway travel time prediction with state-space neural networks. Transport. Res. Rec. 2002, 1811:30-39.
28. Vlahogianni E.I., Karlaftis M.G., Golias J.C. Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transport. Res. Part C: Emerg. Technol. 2005, 13(3):211-234.
29. Wang Y., Papageorgiou M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transport. Res. Part B: Methodol. 2005, 39(2):141-167.
30. Whitham G.B. Linear and Nonlinear Waves 1974, John Wiley, NY.
31. Williams B.M. Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modeling. Transport. Res. Rec. 2001, 1776:194-200.
32. Williams B.M., Durvasula P.K., Brown D.E. Urban traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transport. Res. Rec. 1998, 1644:132-144.
33. Yeon J., Elefteriadou L., Lawphongpanich S. Travel time estimation on a freeway using discrete time Marko chains. Transport. Res. Part B: Methodol. 2008, 42(2):325-338.
34. Zeng X., Zhang Y. Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling. Comput. - Aid. Civ. Infrastruct. Eng. 2013, 28(5):359-371.
35. Zhang X., Rice J. Short-term travel time prediction. Transport. Res. Part C: Emerg. Technol. 2003, 11(3-4):187-210.
36. Zhang H.J., Ritchie S.G., Lo Z.P. Macroscopic modeling of freeway traffic using an artificial neural network. Transport. Res. Rec. 1997, 1588:110-119.
37. Zheng W., Asce M.L.D., Shi Q. Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J. Transport. Eng. 2006, 132(2):114-121.