Random Process Model for Urban Traffic Flow Using a Wavelet-Bayesian Hierarchical Technique

Computer-Aided Civil and Infrastructure Engineering

Volumn 25, Issue 8, 2010, Pages 613-624

  Ghosh, Bidisha ^a   Basu, Biswajit ^a   O'Mahony, Margaret ^a  

^a TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; CONTINUOUS DATA; HIERARCHICAL TECHNIQUES; ON CURRENTS; TRAFFIC CONDITIONS; TRAFFIC FLOW; TRAFFIC FORECASTING; TRAFFIC VOLUMES; URBAN TRAFFIC FLOW;

BAYESIAN NETWORKS; FORECASTING; RANDOM PROCESSES; TRAFFIC SURVEYS; WAVELET ANALYSIS;

STREET TRAFFIC CONTROL;

ALGORITHM; FORECASTING METHOD; HIERARCHICAL SYSTEM; NUMERICAL MODEL; TRAFFIC MANAGEMENT; URBAN TRANSPORT; WAVELET ANALYSIS;

COUNTY DUBLIN; DUBLIN [COUNTY DUBLIN]; IRELAND; LEINSTER;

EID: 78651110324     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/j.1467-8667.2010.00681.x     Document Type: Article

References (45)

1. Adeli, H. & Ghosh-Dastidar, S. (2004), Mesoscopic-wavelet freeway work zone flow and congestion feature extraction model, Journal of Transportation Engineering, ASCE, 130(1), 94-103.
2. Adeli, H. & Karim, A. (2000), Fuzzy-wavelet RBFNN model for freeway incident detection, Journal of Transportation Engineering, ASCE, 126, 464-71.
3. Adeli, H. & Karim, A. (2005), Wavelets in Intelligent Transportation Systems, John Wiley & Sons, UK
4. Adeli, H. & Samant, A. (2000), An adaptive conjugate gradient neural network-wavelet model for traffic incident detection, Computer-Aided Civil Infrastructure Engineering, 15, 251-60.
5. Ashok, K. & Ben-Akiva, M. (2000), Alternative approaches for real-time estimation and prediction of time-dependent origin-destination flows, Transportation Science, 34(1), 21-36.
6. Boto-Giralda, D., Díaz-Pernas, F. J, González-Ortega, D., Díez-Higuera, J. F., Antón-Rodríguez, M. & Martínez-Zarzuela, M. (2010), Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks, Computer-Aided Civil and Infrastructure Engineering, 25(7), 530-45.
7. Carlin, B. P. & Louis, T. A. (1996), Bayes and Empirical Bayes Methods for Data Analysis, Chapman & Hall, New York
8. Cetin, M. & Comert, G. (2006), Short-term traffic flow prediction with regime switching models, Transportation Research Record, 1965, 23-31.
9. Chatfield, C. (2001), Time-Series Forecasting, Chapman and Hall/CRC, London
10. Daubechies, I. (1992), Ten Lectures on Wavelets, SIAM, Philadelphia
11. Geman, S. & Geman, D. (1984), Stochastic relaxation, Gibbs distributions and Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-41.
12. Ghosh, B., Basu, B. & O'Mahony, M. M. (2006), Analysis of trend in traffic flow, in Proceedings of Institute of Engineering and Technology: Irish Signals and Systems Conference, Dublin, Ireland, 415-19.
13. Ghosh, B., Basu, B. & O'Mahony, M. M. (2007), A Bayesian time-series model for short-term traffic flow forecasting, Journal of Transportation Engineering, ASCE, 133(3), 180-89.
14. Ghosh, B., Basu, B. & O'Mahony, M. (2008), Wavelet-Bayesian hierarchical stochastic model for short-term traffic flow at noncritical junctions, Proceedings of the 87th Annual Meeting of Transportation Research Board (CD-ROM), TRB, Washington DC
15. Ghosh-Dastidar, S. & Adeli, H. (2006), Neural network-wavelet micro-simulation model for delay and queue length estimation at freeway work zones, Journal of Transportation Engineering, ASCE, 132(4), 331-41.
16. Hamad, K., Lee, E., Shourijeh, M. T. & Faghri, A. (2009), Near-term travel time prediction utilizing Hilbert-Huang transform, Computer-Aided Civil and Infrastructure Engineering, 24(8), 551-76.
17. Hastings, W. K. (1970), Monte Carlo sampling methods using Markov chains and their application. Biometrika, 57, 97-100.
18. Huang, D. (2003), Wavelet analysis in a traffic model, Physica A: Statistical Mechanics and Its Applications, 329(1-2), 298-308.
19. Jiang, X. & Adeli, H. (2004), Wavelet packet-autocorrelation function method for traffic flow pattern analysis, Computer-Aided Civil and Infrastructure Engineering, 19(5), 324-37.
20. Jiang, X. & Adeli, H. (2005), Dynamic wavelet neural network for traffic flow forecasting, Journal of Transportation Engineering, ASCE, 131(10), 771-79.
21. Kamarianakis, Y., Kanas, A. & Prastacos, P. (2005), Modeling traffic volatility dynamics in an urban network, Transportation Research Record, 1923, 18-27.
22. Karim, A. & Adeli, H. (2002a), Incident detection algorithm using wavelet energy representation of traffic patterns, Journal of Transportation Engineering, ASCE, 128, 232-42.
23. Karim, A. & Adeli, H. (2002b), Comparison of the fuzzy-wavelet RBFNN freeway incident detection model with the California algorithm, Journal of Transportation Engineering, ASCE, 128(1), 21-30.
24. Karim, A. & Adeli, H. (2003), Fast automatic incident detection on urban and rural freeways using the wavelet energy algorithm, Journal of Transportation Engineering, ASCE, 129(1), 57-68.
25. Kesting, A. & Treiber, M. (2008), How reaction time, update time, and adaptation time influence the stability of traffic flow, Computer-Aided Civil and Infrastructure Engineering, 23(2), 125-37.
26. Lee, P. M. (1997), Bayesian Statistics: An Introduction, 2nd edn. Arnold Publication, A member of the Hodder Headline group, London
27. Mallat, S. (1998), A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, London
28. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. (1953), Equation of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087-92.
29. Qiao, F., Wang, X. & Yu, L. (2003), Optimizing aggregation level for ITS data based on wavelet decomposition, in Proceedings of the 82nd Annual Meeting of Transportation Research Board (CD-ROM), TRB, Washington DC
30. Queen, C. & Albers, C. (2009), Intervention and causality: forecasting traffic flows using a dynamic Bayesian network, Journal of the American Statistical Association, 104(486), 669-81.
31. Samant, A. & Adeli, H. (2000), Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis, Computer-Aided Civil Infrastructure Engineering, 15, 241-50.
32. Samant, A. & Adeli, H. (2001), Enhancing neural network incident detection algorithms using wavelets, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45.
33. Stathopoulos, A., Dimitriou, L. & Tsekeris, T. (2008), Fuzzy modeling approach for combined forecasting of urban traffic flow, Computer-Aided Civil and Infrastructure Engineering, 23(7), 521-35.
34. Sun, H., Xiao, H., Yang, F., Ran, B., Tao, Y. & Oh, Y. (2005), Wavelet preprocessing for local linear traffic prediction, in Proceedings of the 85th Annual Meeting of Transportation Research Board, (CD-ROM), TRB, Washington DC
35. Tebaldi, C., West, M. & Karr, A. F. (2002), Statistical analysis of freeway traffic flows, Journal of Forecasting, 21, 39-68.
36. Teng, H. & Qi, Y. (2003), Application of wavelet technique to freeway incident detection, Transportation Research Part C: Emerging Technologies, 11, 289-308.
37. Tsekeris, T. & Stathopoulos, A. (2006), Real-time traffic volatility forecasting in urban arterial networks, Transportation Research Record, 1964, 146-56.
38. Venkatanarayana, R., Smith, B. L. & Demetsky, M. J. (2006), Traffic pattern identification using wavelet transforms, in Proceedings of the 84th Annual Meeting of Transportation Research Board (CD-ROM), TRB, Washington DC
39. Vlahogianni, E. I. (2008), Short-term predictability of traffic flow regimes in signalised arterials, Road and Transport Research, 17(2), 19-33.
40. Vlahogianni, E. I., Golias, J. C. & Karlaftis, M. G. (2004), Short-term forecasting: overview of objectives and methods, Transport Reviews, 24(5), 533-57.
41. Vlahogianni, E. I., Karlaftis, M. G. & Golias, J. C. (2007), Spatio-temporal short-term urban traffic flow forecasting using genetically-optimized modular networks, Computer-Aided Civil and Infrastructure Engineering, 22(5), 317-25.
42. Vlahogianni, E. I., Karlaftis, M. G. & Golias, J. C. (2008), Temporal evolution of short-term urban traffic flow: a non-linear dynamics approach, Computer-Aided Civil and Infrastructure Engineering, 23(7), 536-48.
43. Xie, Y., Zhang, Y. & Ye, Z. (2007), Short-term traffic volume forecasting using kalman filter with discrete wavelet decomposition, Computer-Aided Civil and Infrastructure Engineering, 22, 326-34.
44. Yang X. & Recker, W. W. (2008), Evaluation of information applications of a self-organizing distributed traffic information system for a large-scale real-world traffic network, Computer-Aided Civil and Infrastructure Engineering, 23(8), 575-95.
45. Zhang, C., Sun, S. & Yu, G. (2004), A Bayesian network approach to time series forecasting of short-term traffic flows, in Proceedings of the 7th IEEE International Conference on Intelligent Transportation Systems (ITSC2004), Washington DC, 216-21.