Quantitative Analysis of Vehicular Traffic Flow Order at Signalized Intersections

A quantitative evaluation of the traffic flow order is necessary to improve the operational level at intersections. To date, how to assess the subjective perceptions of the traffic flow order at intersections is unclear. This study develops a visual analog scale (VAS) and an artificial intelligence algorithm to explore the subjective and objective evaluation methods of the traffic flow order at signalized intersections, respectively. First, a subjective measurement method is developed based on the VAS by evaluating 100 video clips from 24 intersections in Shanghai. Then, an objective estimation model for the vehicular traffic flow order evaluation is constructed based on a multilayer perceptron neural network (MLP). A model parameter-hyperparameter joint optimization method is proposed. The results show that the developed method for the traffic flow order at intersections has a coefficient of determination (R2) of 0.83 and an average absolute error of 5.78 compared with the subjective evaluation.