  Connected vehicles (CVs) can provide numerous new data via vehicle-to-vehicle
or vehicle-to-infrastructure communication. These data can in turn be used to
facilitate real-time traffic state estimation. In this paper, we focus on ramp
queue length estimation in a connected vehicle environment, which improves
control design and implementation of ramp metering algorithms. One major
challenge of the estimation problem is that the CV data only represent partial
traffic observations and could introduce new uncertainties if real-time CV
penetration rates are unknown. To address this, we build our estimation
approach on both traditional freeway sensors and new CV data. We first
formulate a ramp queue model that considers i) variations in the penetration
rate and ii) noise in measurements. Then we develop a robust filter that
minimizes the impacts of these two kinds of uncertainties on queue estimation.
More importantly, we show that the designed filter has guaranteed long-term
estimation accuracy. It allows us to quantify in a theoretical way the
relationship between estimation error and fluctuation of CV penetration rates.
We also provide a series of simulation results to verify our approach.
