Traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the
presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density
functions for the states. Often centralized architectures face challenges due to high communication demands. This talk will present recently developed new estimation
techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability
density functions of the high dimensional traffic state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efficient way. The
proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efficiency of the PPFs
and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized.