Name
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. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | bayesian implementation,lagrangian macroscopic traffic flow model,state-space methods,kalman filters,regime-based kalman filter,bayes methods,state estimation,switched linear state-space formulation,microscopic traffic flow simulation,traffic pattern reconstruction,state estimation technique,sequential monte carlo filtering,monte carlo methods,linear systems,time-evolution,road traffic,rkf |
Field | DocType | ISSN |
Linear system,Computer science,Particle filter,Artificial intelligence,Monte Carlo method,Mathematical optimization,Traffic flow,Pattern recognition,Macroscopic traffic flow model,Filter (signal processing),Algorithm,Kalman filter,Monte Carlo molecular modeling | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Won Yoon | 1 | 112 | 23.94 |
| Tigran T. Tchrakian | 2 | 40 | 6.88 |