Real-time estimation of aggregated traffic states of multi-region urban networks

The problem of traffic state estimation for large-scale urban networks is studied. Given a network that is partitioned in a number of regions, the aggregated traffic dynamics describe the vehicle accumulation in each region as well as the transfer flows among neighbouring regions. Considering the fact that many such models have been extensively used for control in the literature recently, this work tackles the real-time estimation problem when limited data are available. An estimation engine is developed according to the Extended Kalman Filter (EKF) theory, that tries to estimate the real state of the multi-region dynamic system based on sparse traffic sensors measurements. First, a stochastic model is presented for the dynamics of the process (plant). Then, the EKF estimation scheme is described that is based on a simpler aggregated model of the dynamics and some real-time measurements. The accuracy of the estimations is investigated through simulation by studying a realistic configuration of real-time availability of measurements; however the developed methodology is generic and the vector state we seek to estimate as well as the available measurements can be altered according to the application. The resulting estimated traffic states (i.e., regional accumulations, demands, and distribution of outflows) are compared to the real ones that are obtained from the stochastic plant. The developed algorithm can be utilized by closed-loop online urban traffic management strategies to feed back to the controller the estimated traffic state.