Prediction of queuing length at metering roundabout using adaptive neuro fuzzy inference system

Hong Ki An,Ahmed N Abdalla

doi:10.1177/0020294019839415

Abstract

A metering roundabout where traffic is controlled by signals where phase times are influenced by queue detector occupancy may be the solution to reduce queue lengths under unbalanced traffic flows. In the past decades, a number of studies have attempted to evaluate the effectiveness of metering roundabout, especially on the dominant approach. Little studies, however, have been directed on prediction of the queuing lengths, which is essential to determine the detector locations. This paper introduces a queue length estimation model using adaptive neuro fuzzy inference system for unbalanced roundabout traffic flows. The adaptive neuro fuzzy inference system model consists of an input layer representing four parameters as arrival volumes, conflicting volumes, phase green and red time, and output layer with four neuron representing queuing length. MATLAB software and additional statistical tests were used as the tool to develop the models for the data. In order to conduct credible model validations, model output data were compared against the observed data collected using drones. The results from the analysis demonstrated that adaptive neuro fuzzy inference system model is able to estimate the queuing length at metering roundabouts. Thus, it is expected that the adaptive neuro fuzzy inference system model will help practitioners in determining optimal detector locations and will be a foundation research for roundabouts with signals.

Highlights

In regard to a metering roundabout, the determination of the detector location is very important, because queuing lengths on each approach can be affected by when the signal should be actuated and the signal’s green/red time, which is determined by when the queue hits the detector on the controlling approach.[1,2]
Let R be a crisp relation between the two sets X = {arrival volumes, conflicting volumes, Pgreen time and Pred time} and Y = {Queuing length}, such that R: X!Y and R consists of the pairs
This paper attempted to use adaptive neuro fuzzy inference system (ANFIS) models to predict the queuing length on each approach at metering roundabout based on four parameters, which mainly affect the queuing length on each approach; that is, arrival volumes, conflicting volumes, phase green and red times

Summary

Introduction

In regard to a metering roundabout, the determination of the detector location is very important, because queuing lengths on each approach can be affected by when the signal should be actuated and the signal’s green/red time, which is determined by when the queue hits the detector on the controlling approach.[1,2]. According to past research, unbalanced traffic flow conditions can exacerbate the roundabout operational performance. In such cases, the metering roundabout can be a solution leading to a reduction in vehicle delay times. There has been relatively few research investigating queuing length estimation, which is an important parameter affecting the optimal detector locations at the roundabout. This study investigates the operation of metering roundabout and predicts the queuing length on each approach using adaptive neuro fuzzy inference system (ANFIS). To achieve this purpose, data collected by drones are used in the process of model calibration and validation. The proposed model structure was optimized and validated using R2 and RMSE tests methods

Background

Findings

Result and discussion

Conclusion and future work