Fault-Adaptive Traffic Density Estimation for the Asymmetric Cell Transmission Model

The often faulty nature of measurement sensors hinders reliable traffic state estimation, affecting in this way various transportation operations such as traffic control and in-car navigation. This work proposes a systematic, model-based, online and network-wide approach to achieve robust and good quality state estimation in the presence of sensor faults. The approach is comprised of three stages aiming at 1) identifying the level of faulty behavior of each sensor using a novel fault-tolerant optimization algorithm, 2) isolating faults, and 3) improving state estimation performance by adaptively compensating sensor faults and resolving the state estimation problem. The approach is examined in the context of the Asymmetric Cell Transmission Model for freeway traffic density estimation. Simulation results demonstrate the effectiveness of the proposed fault-adaptive approach, yielding estimation performance very close to the one obtained with healthy measurements, irrespective of the fault magnitude. It is further illustrated that the developed fault-tolerant optimization algorithm can simultaneously identify different types of faults from multiple sensors.