Abstract
In this paper we apply state-estimation techniques to a model which describes the time-evolution of observed traffic patterns. We develop a switched linear state-space formulation of a macroscopic traffic flow model and then use Sequential Monte Carlo filtering and regime-based Kaiman Filter (RKF) to reconstruct the underlying traffic patterns, where observations are provided by a microscopic traffic flow simulation which runs in parallel with our model.
| Original language | English |
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| Title of host publication | ICPR 2012 - 21st International Conference on Pattern Recognition |
| Pages | 214-217 |
| Number of pages | 4 |
| Publication status | Published - 2012 |
| Event | 21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan Duration: 2012 Nov 11 → 2012 Nov 15 |
Publication series
| Name | Proceedings - International Conference on Pattern Recognition |
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| ISSN (Print) | 1051-4651 |
Other
| Other | 21st International Conference on Pattern Recognition, ICPR 2012 |
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| Country | Japan |
| City | Tsukuba |
| Period | 12/11/11 → 12/11/15 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
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Cite this
Yoon, J. W., & Tchrakian, T. T. (2012). Bayesian implementation of a Lagrangian macroscopic traffic flow model. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 214-217). [6460110] (Proceedings - International Conference on Pattern Recognition).