Distributed Traffic State Estimation in V2X-Enabled Connected Vehicle Networks

TL;DR

This paper introduces a distributed traffic state estimation method using V2X communication and a tailored Kalman filter, effectively capturing traffic dynamics with sparse sensors and intermittent connectivity.

Contribution

It develops a novel distributed Kalman filter with consensus and projection steps for accurate traffic estimation in V2X-enabled networks, handling heterogeneity and physical constraints.

Findings

Accurately estimates shockwave traffic dynamics in simulations.

Performance degrades gracefully with lower vehicle penetration rates.

Reveals phase transitions in network observability based on connectivity.

Abstract

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.