Turning Rate Estimation in Roundabouts: Analysis and Validation of Different Estimation Methods

Abstract

The knowledge of turning rates in roundabouts is a crucial element of traffic modeling. Measuring the turning movements is often carried out by manual traffic counts (noting on paper or using handheld devices), which is a labor-intensive, therefore expensive process. The aim of this paper is the examination and comparison of different estimation methods used for turning rates in roundabouts. Traditional iteration based approach as well as estimators adopted from control theory are discussed, benchmarked, and validated on real-world traffic data. For the estimation procedures, the traffic flows (measured at each leg of the intersection) are the input. In this way, the traditional origin-destination traffic count at an intersection can be substituted by automated traffic detection at the cross-sections together with the adequately implemented estimation process (suggested in the paper). The calibration of estimation methods is of crucial importance as well. The calibration is demonstrated based on real-world traffic counts at roundabouts. The different methods have been compared using different error metrics. As a main finding of the research, it is shown that, given the right tuning, constrained Kalman Filtering outperforms the unconstrained Kalman Filtering and the traditional iterative procedure.