TY - GEN
T1 - Bayesian implementation of a Lagrangian macroscopic traffic flow model
AU - Yoon, Ji Won
AU - Tchrakian, Tigran T.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84874557605
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 214
EP - 217
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -