Parallelized particle filtering for freeway traffic state tracking

2007 European Control Conference, ECC 2007

Volumn , Issue , 2007, Pages 2442-2449

  Hegyi, A ^a   Mihaylova, L ^c   Boel, R ^b   Lendek, Zs ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b GHENT UNIVERSITY   (Belgium)

^c LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Freeway traffic state tracking; Parallel particle filters

Indexed keywords

HIGHWAY TRAFFIC CONTROL; MONTE CARLO METHODS;

COMPUTATIONAL DEMANDS; ESTIMATION PROBLEM; FREEWAY TRAFFIC; GENERAL NONLINEAR SYSTEMS; NON-GAUSSIAN NOISE; PARTICLE FILTER; PARTICLE FILTERING; STATE TRACKING;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84927728756     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/ecc.2007.7069059     Document Type: Conference Paper

References (17)

1. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174-188, February 2002.
2. M. Bolic, P. M. Djuric, and S. Hong. Resampling algorithms and architectures for distributed particle filter. IEEE Trans. Signal Processing, 53:2442-2450, 2005.
3. B. P. Carlin, N. G. Polson, and D. S. Stoffer. A Monte Carlo approach to nonnormal and non-linear state-space modelling. Journal of the American Statistical Association, 87(418):493-500, 1992.
4. C. Coates. Distributed particle filtering for sensor networks. In Proc. of the Int. Symp. Information Processing in Sensor Networks, Berkeley, California, April 2004.
5. A. Doucet, J. F. G. de Freitas, and N. J. Gordon, editors. Sequential Monte Carlo Methods in Practice, chapter Improving Regularised Particle Filters. Springer-Verlag, New York, 2001.
6. A. Hegyi, B. De Schutter, and J. Hellendoorn. Optimal coordination of variable speed limits to suppress shock waves. IEEE Transactions on Intelligent Transportation Systems, 6(1):102-112, March 2005.
7. A. Hegyi, D. Girimonte, R. Babuska, and B. De Schutter. A comparison of filter configurations for freeway traffic state estimation. In Proceedings of the International IEEE Conference on Intelligent Transportation Systems 2006, pages 1029-1034, Toronto, Canada, September 17-20 2006.
8. J. S. Liu and R. Chen. Sequential Monte Carlo methods fory dynamical system. Journal of the American Statistical Association, 93:1032-1044, 1998.
9. S. Maskell, K. Weekes, and M. Briers. Distributed tracking of stealthy targets using particle filters. In Proc. of IEE seminar on target tracking: Algorithms and applications, pages 13-20. IEE, Birmingham, UK, March 2006.
10. L. Mihaylova and R. Boel. A particle filter for freeway traffic estimation. In Proceedings of the 43rd IEEE Conference on Decision and Control, pages 2106-2111, Atlantis, Paradise Island, Bahamas, 2004.
11. L. Mihaylova, R. Boel, and A. Hegyi. Freeway traffc estimation within particle filtering framework. Automatica, 43(2):290-300, 2007.
12. B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman filter. Artech House, 2004. ISBN: 1-58053-631.
13. X. Sheng, Y. H. Hu, and P. Ramanathan. Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network. In 4th Intl. Conf. on Information Processing in Sensor Networks (IPSN), pages 181-188, 2005.
14. X. Sun, L. Mu noz, and R. Horowitz. Mixture Kalman filter based highway congestion mode and vehicle density estimator and its application. In Proceedings of the American Control Conference, pages 2098-2103, Boston, USA, 2004.
15. Y. Wang and M. Papageorgiou. An adaptive freeway traffic state estimator and its real-data testing-Part I: Basic properties. In Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, pages 531-536, Vienna, Austria, September 13-16, 2005.
16. Y. Wang and M. Papageorgiou. An adaptive freeway traffic state estimator and its real-data testing-Part II: Adaptive capabilities. In Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, pages 537-548, Vienna, Austria, September13-16 2005.
17. Y. Wang and M. Papageorgiou. Real-time freeway traffic state estimation based on extended Kalman filter: A general approach. Transportation Research Part B: Methodological, 39(2):141-167, 2005.