Joint parameter and state estimation algorithms for real-time traffic monitoring

A common approach to traffic monitoring is to combine a macroscopic traffic flow model with traffic sensor data in a process called state estimation, data fusion, or data assimilation. The main challenge of traffic state estimation is the integration of various types of sensor data (e.g. speed, flow, travel time, etc.) into the flow model due to the nonlinearities of the traffic model. When parameters are also estimated, the nonlinearity of the estimation problem increases, motivating the development of advanced estimation algorithms to handle the additional nonlinearity. To improve performance of traffic state estimation algorithms this work investigates the problem of simultaneously or jointly estimating both the traffic state and the parameters of the traffic model. It uses two new traffic parameter and state estimation algorithms based on multiple model particle filtering, and multiple model particle smoothing. Because incidents on freeways can be modeled through parameter changes in the traffic model, this work applies both algorithms to the problem of incident detection.