Predicting Roundabout Lane Capacity using Artificial Neural Networks.

Several roundabout capacity methods and approaches have been proposed until now. They are mainly based either on regression equations of observed capacity and gap acceptance or on stochastic models through simulation techniques. However, all of them rely on different variables and factors and are being adapted to local driving behavior. Hence, it is not clear if existing techniques are appropriate for reliable capacity estimations and optimal design of Greek roundabouts. This paper presents the results of an experimental research that has been conducted as a first step in the optimization of roundabouts capacity estimation, based on a dataset of 11 roundabouts in Greece. The study firstly aims to understand what geometric roundabout features and driving behavior parameters influence capacity. Artificial neural networks (NN) were tested and developed to predict accurate roundabout capacities. Appropriate roundabouts were selected for the analysis and their operational performance was filmed using a UAV and a stabilized camera during peak periods. Video image processing techniques and algorithms allowed the extraction of empirical data (traffic flows, gap acceptance parameters) and accurate geometric characteristics. The results demonstrate that artificial neural networks can predict the capacity of roundabouts accurately. [ABSTRACT FROM AUTHOR]