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

TL;DR

This study demonstrates that applying Kalman and Particle filters to connected vehicle data significantly improves queue length estimation accuracy, especially at low market penetration rates, enabling real-time traffic management.

Contribution

It introduces multilevel filtering techniques to enhance low-level parameter estimation in connected vehicle-based queue models, outperforming traditional methods.

Findings

Kalman and Particle filters estimate true parameters within 15 minutes.

Filtering algorithms outperform known parameter scenarios at low market penetration.

Real-time filtering achieves less than 0.1 second computational time.

Abstract

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.