In this talk we consider the problem of estimating the state of traffic flow using filtering techniques that rely on an analytical model of traffic flow. The goal is to get as accurate estimation as possible of the current traffic conditions based on the sparse and noisy measurement from in-ground induction loop detectors. In practice this information is provided to travelers, who make decisions on routes, and transportation system managers that use it for forecasting traffic conditions for the next 15-30 minutes in order to apply appropriate control strategies, such as route guidance or flow control through ramp metering. Existing filtering algorithms are limited in their ability to properly capture the nonlinear nature of the system dynamics as well as non-Gaussian sensor models. Here we develop and apply a computationally efficient particle filter based algorithm to address this problem. We apply our algorithm to a data set of measurements from the Illinois interstate highway system. We also provide overview of traffic flow models used in practice and POLARIS transport system simulator developed at Argonne.