Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

Abstract

Abstract-Traffic state estimation is an important problem with significant applications in advanced traveler information systems, transportation management and traffic control. Nonetheless, the often faulty nature of measurement sensors, especially inductive loop detectors, hinders reliable state estimation. This work proposes a systematic, model-based, networkwide, moving-horizon approach for fault-tolerant traffic state estimation. By exploiting information redundancy and fault sparsity, it achieves reliable estimation and simultaneously detects, isolates and corrects measurements from periods of faulty sensor behavior. The approach is examined in relation to the Asymmetric Cell Transmission Model, a popular and powerful macroscopic first-order traffic flow model. In the absence of any faults, the proposed approach achieves similar results with other state-of-the-art estimation approaches while it can achieve better estimation performance when some sensors are faulty. It is further demonstrated that the developed approach can successfully handle multiple faults of different types.

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

“…In general, methods for traffic state estimation can be categorized into model-driven and data-driven. In model-driven traffic state estimation, statistical state estimators such as Particle Filter [19]- [21], Kalman Filter [22], [23], Extended Kalman Filter (EKF) [24]- [27], Unscented Kalman Filter (UKF) [28], [29], and Ensemble Kalman Filter (EnKF) [11], [20], [30], [31] are among of the most extensively used methods-see [32, Tables 1 and 2] for a list of state estimators used in the recent literature. To mention a few, traffic density estimation has been studied based on a switching-mode scheme of cell transmission model (CTM) [33].…”

Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances

“…The ACTM finds its place in the traffic state estimation literature in few studies pertaining to density estimation on freeways. In [11] and [12], for instance, the authors propose a moving-horizon approach for sensor-fault-tolerant traffic state estimation using the ACTM. Besides these, there are several other studies related to traffic state estimation using other variants of the CTM, references to which can be found in [13].…”

Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark

“…Reliable state/parameter estimation in the presence of faults highly relies on accurate and prompt fault detection and isolation (FDI). The combination of these two tasks is referred to as fault-tolerant estimation (FTE) in some literature, e.g., the multiple-model approach in [2]- [4], the adaptive Kalman filtering approach in [5], [6], and the moving horizon estimation approach exploiting a sparsity constraint on faults in [7]. FTE is also an important part in a fault-tolerant control system [8], [9].…”