  Estimation models from connected vehicles often assume low level parameters
such as arrival rates and market penetration rates as known or estimate them in
real-time. At low market penetration rates, such parameter estimators produce
large errors making estimated queue lengths inefficient for control or
operations applications. In order to improve accuracy of low level parameter
estimations, this study investigates the impact of connected vehicles
information filtering on queue length estimation models. Filters are used as
multilevel real-time estimators. Accuracy is tested against known arrival rate
and market penetration rate scenarios using microsimulations. To understand the
effectiveness for short-term or for dynamic processes, arrival rates, and
market penetration rates are changed every 15 minutes. The results show that
with Kalman and Particle filters, parameter estimators are able to find the
true values within 15 minutes and meet and surpass the accuracy of known
parameter scenarios especially for low market penetration rates. In addition,
using last known estimated queue lengths when no connected vehicle is present
performs better than inputting average estimated values. Moreover, the study
shows that both filtering algorithms are suitable for real-time applications
that require less than 0.1 second computational time.
