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Estimation models from connected vehicles often assume low level parameterssuch as arrival rates and market penetration rates as known or estimate them inreal-time. At low market penetration rates, such parameter estimators producelarge errors making estimated queue lengths inefficient for control oroperations applications. In order to improve accuracy of low level parameterestimations, this study investigates the impact of connected vehiclesinformation filtering on queue length estimation models. Filters are used asmultilevel real-time estimators. Accuracy is tested against known arrival rateand market penetration rate scenarios using microsimulations. To understand theeffectiveness for short-term or for dynamic processes, arrival rates, andmarket penetration rates are changed every 15 minutes. The results show thatwith Kalman and Particle filters, parameter estimators are able to find thetrue values within 15 minutes and meet and surpass the accuracy of knownparameter scenarios especially for low market penetration rates. In addition,using last known estimated queue lengths when no connected vehicle is presentperforms better than inputting average estimated values. Moreover, the studyshows that both filtering algorithms are suitable for real-time applicationsthat require less than 0.1 second computational time.

Cycle-to-Cycle Queue Length Estimation from Connected Vehicles with Filtering on Primary Parameters