Traffic State Estimation from Aggregated Measurements with Signal Reconstruction Techniques

Abstract

The estimation of the state of traffic provides a detailed picture of the conditions of a traffic network based on limited traffic measurements and, as such, plays a key role in intelligent transportation systems. Most of the existing state estimation algorithms are based on Kalman filtering and its variants, which, starting from the current estimate, predict the future state and then correct it on the basis of new measurements. Most often, traffic measurements are aggregated over multiple time steps, and this procedure raises the question of how to best use this information for state estimation. A standard approach that performs the correction only at the time step when the aggregated measurement is received is suboptimal. Reconstructing the high-resolution measurements from the aggregated ones and using them to correct the state estimates at every time step are proposed. Several reconstruction techniques from signal processing, including kernel regression and a reconstruction approach based on convex optimization, were considered. The proposed approach was evaluated on real-world NGSIM data collected at Interstate 101, located in Los Angeles, California. Experimental results show that signal reconstruction leads to more accurate traffic state estimation as compared with the standard approach for dealing with aggregated measurements.