Queue Length Estimation at Signalized Intersection Based on Ensemble Learning

Abstract:

Based on license plate recognition （LPR） data and connected vehicle trajectory data， an ensemble learning method was deployed to estimate the intersection queue length. By analyzing the applicability and accuracy of different queue length estimation methods， the random forest method was applied to design a base ensemble learner and formulize the nonlinear mapping relationship among the LPR data， connected vehicle trajectory data， estimation results of the existing queue length methods and real queue length values. Simulation results show that the proposed method overperforms the existing queue length methods， with a mean absolute error of 1.3 m?cycle^-1?lane^-1 and a mean absolute percent error of 1.4%.