Modelling the variation and uncertainty problem of right-turn-on-red queue in a variety of conflicting environments

The primary purpose of this study is to develop an explicit dynamical state estimation model of the right-turn-on-red queue to solve the accuracy problem of the right-turn-on -red operation assessment in a variety of conflicting environments. Our effort focuses on characterizing the dynamics and nonlinearity of the right-turn-on-red queue. The proposed model can explicitly measure the variability of the right-turn-on-red queue under various traffic conditions without requiring substantial parameters. In this paper, we provide a set of measures to formulate the right-turn-on-red operation performance. Besides some fun-damental average indices, the index of the signal-state-dependent queue length that can characterize the queue evolution process of right-turn-on-red is obtained, in particular, we also obtain the variance of right-turn-on-red queue length at a given time point within a cycle. The new version of the queue length along the signal state can play an important role in understanding the accuracy of the use of right-turn-on-red operation and providing a high quality solution to properly treat the right-turn-on-red control under the different types of right-turn-on-red phasing regimes. The results obtained from the proposed model were compared with field data, which showed good agreement. In addition, a series of numerical experiments is carried out to clarify the effects of the possible factors on the dynamical state of the right-turn-on-red queue.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The study aims to develop a dynamic state estimation model for the right-turn-on-red queue to address the accuracy problem in various conflicting environments. The proposed model characterizes the dynamics and nonlinearity of the queue and explicitly measures its variability under different traffic conditions. It provides measures to evaluate the right-turn-on-red operation performance, including the queue length evolution process and variance at a given time point within a cycle. The results of the proposed model show good agreement with field data, and numerical experiments clarify the effects of possible factors on the queue's dynamic state.