2025/12/07 by de Heij, Vincent, Niazi, M. Umar B., Ahmed, Saeed +1
Engineering · #FOS: Electrical engineering #Systems and Control (eess.SY) #Traffic Prediction and Management Techniques #Traffic control and management #Vehicular Ad Hoc Networks (VANETs) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2512.06765
openalex publication_date 2025/12/07 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/28
This paper presents a distributed traffic state estimation framework in which infrastructure sensors and connected vehicles act as autonomous, cooperative sensing nodes. These nodes share local traffic estimates with nearby nodes using Vehicle-to-Everything (V2X) communication. The proposed estimation algorithm uses a distributed Kalman filter tailored to a second-order macroscopic traffic flow model. To achieve global state awareness, the algorithm employs a consensus protocol to fuse heterogeneous spatiotemporal estimates from V2X neighbors and applies explicit projection steps to maintain physical consistency in density and flow estimates. The algorithm's performance is validated through microscopic simulations of a highway segment experiencing transient congestion. Results demonstrate that the proposed distributed estimator accurately reconstructs nonlinear shockwave dynamics, even with sparse infrastructure sensors and intermittent vehicular network connectivity. Statistical analysis explores how different connected vehicle penetration rates affect estimation accuracy, revealing notable phase transitions in network observability.