Dynamic traffic prediction for insufficient data roadways via automatic control theories

Control Engineering Practice

Volumn 19, Issue 12, 2011, Pages 1479-1489

  Chang, Tang Hsien ^a   Chueh, Chia Hung ^a   Yang, Li Kai ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Automatic control theory; Dynamic section flow estimation; Insufficient data source; Observer design; Traffic prediction

Indexed keywords

DATA SOURCE; DYNAMIC SECTION; DYNAMIC TRAFFIC; FEASIBLE ALGORITHMS; FLOW PREDICTION; METROPOLITAN AREA; NUMERICAL EVALUATIONS; OBSERVER DESIGN; TRAFFIC DATA COLLECTION; TRAFFIC DETECTORS; TRAFFIC ESTIMATION; TRAFFIC PREDICTION; VEHICLE DETECTOR;

ALGORITHMS; AUTOMATION; COMPUTER SIMULATION; CONTROL; CONTROL THEORY; DETECTORS; FORECASTING; HIGHWAY PLANNING; INFORMATION MANAGEMENT; NUMERICAL METHODS; PROCESS CONTROL; SYSTEM THEORY; TRAFFIC CONTROL;

ADVANCED TRAFFIC MANAGEMENT SYSTEMS;

EID: 80054839021     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2011.08.007     Document Type: Article

References (51)

1. Anderson B.D.O., Moore J.B. Optimal control-linear quadratic methods 1990, Prentice-Hall Inc.
2. Brogan W.L. Modern control theory 1991, Prentice Hall, Englewood Cliffs, NJ. 3rd ed.
3. Chang J.L., Miaou S.P. Real-time prediction of traffic flows using dynamic generalized linear models. Transportation Research Record 1999, 1678:168-178.
4. Chen H., Grant-Muller S. Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C 2001, 9:319-336.
5. Davis G.A., Lan C.-J. Estimating intersection turning movement proportions from less-than-complete sets of traffic counts. Transportation Research Record 1994, 1510:53-59.
6. Davis G.A., Nihan N.L. Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering 1991, 117(2):178-188.
7. Delurgio S.A. Forecasting principles and applications 1998, McGraw-Hill.
8. DeVellis R.F. Scale development theory and applications 1991, Sage Publications, Newbury, CA.
9. Dia H. An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 2001, 131:253-261.
10. Fu L., Rilett L.R. Estimation of time-dependent, stochastic route travel times using artificial neural networks. Transportation Planning and Technology 2000, 24(1):25-36.
11. Grimble M.J. Robust industrial control: Optimal design approach for polynomial systems 1994, Prentice Hall.
12. Hamed M.M., Al-Masaeid H.R., Said Z.M.B. Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering 1995, 121(3):249-254.
13. Hu X.J., Wang W., Sheng H. Urban traffic flow prediction with variable cell transmission model. Journal of Transportation Systems Engineering and Information Technology 2010, 10(4):73-78.
14. Ishak S., Alecsandru C. Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings. Journal of Transportation Engineering 2004, 130(4):452-465.
15. Jazwinski A.H. Stochastic processes and filtering theory 1970, Academic Press.
16. Karlaftis M.G., Vlahogianni E.I. Memory properties and fractional integration in transportation time-series. Transportation Research Part C 2009, 17:444-453.
17. Kuo B.C. Discrete-data control systems 1970, Prentice-Hall, Inc.
18. Kuo B.C. Automatic control systems 1991, Prentice-Hall. 6th ed.
19. Lan, C.-J. (2001) Adaptive turning flow estimation based on incomplete detector information for advanced traffic management. In Proceedings of the IEEE Intelligent Transportation Systems Conference, Oakland, California, USA, August 25-29.
20. Lan C.-J., Davis G.A. Real-time estimation of turning movement proportions from partial counts on urban networks. Transportation Research Part C 1999, 7:305-327.
21. Lee S., Fambro D.B. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record 1999, 1678:179-188.
22. Lewis C.D. Industrial and Business Forecasting Method 1982, Butterworth Scientific.
23. Lingras P., Sharma S.C., Osborne P. Traffic volume time-series analysis according to the type of road use. Computer-Aided Civil and Infrastructure Engineering 2000, 15(5):365-373.
24. Luenberger D.G. Introduction to dynamic systems: theory, models, & applications 1979, John Wiley & Sons, New York.
25. Okutani I., Stephanedes Y.J. Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research part B 1984, 18(1):1-11.
26. Park D., Rilett L.R., Han G. Spectral basis neural networks for real-time travel time forecasting. Journal of Transportation Engineering 1999, 125(6):515-523.
27. Pushkar A., Hall F.L., Acha-Daza J.A. Estimation of speeds from single-loop freeway flow and occupancy data using cusp catastrophe theory. Transportation Research Record 1995, 1457:149-157.
28. Quek C., Pasquier M., Lim B.B.S. POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems 2006, 7(2):133-146.
29. Ross P. Exponential filtering of traffic data. Transportation Research Record 1982, 869:43-49.
30. Smith B.L., Demetsky M.J. Short-term traffic flow prediction: Neural network approach. Transportation Research Record 1994, 1453:98-104.
31. Stathopoulos, A., & Karlaftis, M.G. (2001). Spectral and cross-spectral analysis of urban traffic flows. In Proceedings of the IEEE International Conference on Intelligent Transportation Systems. Oakland, California, August 25-29.
32. Stathopoulos A., Karlaftis M.G. A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C 2003, 11:121-135.
33. Sun S.L., Zhang C.S. The selective random subspace predictor for traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 2007, 8(2):367-373.
34. Sun S.L., Zhang C.S., Yu G.Q. A Bayesian network approach to traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 2006, 7(1):124-132.
35. Tan M.C, Wong S.C., Xu J.M., Guan Z.R., Zhang P. An aggregation approach to short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems 2009, 10(1):60-69.
36. Van Der Voort M., Dougherty M., Watson S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C 1996, 4(5):307-318.
37. 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.
38. 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 2005, 13:347-369.
39. Vigos G., Papageorgious M., Wang Y. Real-time estimation of vehicle-count within signalized links. Transportation Research Part C 2008, 16:18-35.
40. 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 2005, 13:211-234.
41. Vlahogianni E.I., Karlaftis M.G., Golias J.C. Temporal evolution of short-term urban traffic flow: a nonlinear dynamics approach. Computer-Aided Civil and Infrastructure Engineering 2008, 23:536-548.
42. Wang Y., Papageorgious D M A real-time freeway network traffic surveillance tool. IEEE Transactions on Control Systems Technology 2006, 14:18-32.
43. Wang Y., Papageorgious M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transportation Research Part B 2005, 39:141-167.
44. Wang Y., Papageorgious M., Messmer A. RENAISSANCE-A unified macroscopic model-based approach to real-time freeway network traffic surveillance. Transportation Research Part C 2006, 14:190-212.
45. Wang Y., Papageorgious M., Messmer A. Real-time freeway traffic state estimation based on extended Kalman filter: A case study. Transportation Science 2007, 41:168-181.
46. Wang Y., Papageorgious M., Messmer A. Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing. Transportation Research Part A 2008, 42:1340-1358.
47. Wang Y., Papageorgious M., Messmer A., Coppola P., Tzimitsi A., Nuzzolo A. An adaptive freeway traffic state estimator. Automatica 2009, 45:10-24.
48. Whittaker I., Garside S, Lindveld K. Tracking and predicting a network traffic process. International Journal of Forecasting 1997, 13:51-61.
49. Yasdi R. Prediction of road traffic using a neural network approach. Neural Computing and Applications 1999, 8:135-142.
50. Zhang H., Ritchie S.G., Lo Z.P. Macroscopic modeling of freeway traffic using an artificial neural network. Transportation Research Record 2000, 1588:110-119.
51. Zhang H.M. Recursive prediction of traffic conditions with neural network models. Journal of Transportation Engineering 2000, 126(6):472-481.