Fault-Adaptive Traffic Demand Estimation Using Network Flow Dynamics

Estimating traffic demand, or origin-destination (OD) matrices, is crucial for transport studies and smart city development. The main objective is to calculate an OD matrix based on available sources (e.g. link traffic counts obtained from traffic sensors) to accurately reproduce field data. A significant complication when using information obtained from traffic sensors, is that such sensors are subject to considerable disruptions impacting data quality and reliability. Despite the extensive study of efficient OD estimation, there is a considerable gap in detecting faulty measurements and identifying faulty sensors within the estimation procedure. This work presents a novel methodology for OD matrix estimation in the presence of faulty measurements. The path-based cell transmission model (CTM) is employed to capture traffic network dynamics within a specified time window, linking link densities with per-path densities and path demand. For the purposes of this work, traffic networks that operate under free-flow conditions are considered and the problem is formulated in an optimisation framework with two distinct variations: 1) no explicit formulation of potential faulty sensors and 2) explicit modelling of potential faulty sensors. Following, a fault-adaptive algorithm is constructed that identifies, isolates and corrects faults to achieve robust demand estimation. The methodology is tested on two realistic literature networks and shows great potential in terms of OD matrix estimation in the presence of faulty measurements. Simulation results underpin the advantage of the proposed approach in terms of performance in estimating quantities of interest as well as identifying the faulty sensors and their fault characteristics.