Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections

Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensive data collection, calibration, and modeling efforts to estimate turning movements. In this study, three models were proposed to estimate turning movements at signalized intersections using approach volumes. Two sets of data from the United States and Canada were obtained to develop and test the proposed models. Machine learning-based regression models, including random forest regressor (RFR) and multioutput regressor (MOR) in addition to an artificial neural network (ANN) model, were developed and trained to analyze the relationship between approach volumes and corresponding turning movements. Multiple evaluation measurements were utilized to compare the models. All models produced satisfactory results. The RFR regression model outperformed the MOR model. However, the ANN model had the best performance when compared to the other models. The proposed models provide traffic engineers and planners with reliable and fast methods to estimate turning movements. & COPY; 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

Effective prediction of turning movement counts at intersections is crucial for various applications, and this study proposes three models using approach volumes to estimate turning movements. Machine learning-based regression models, including random forest regressor (RFR) and multioutput regressor (MOR), as well as an artificial neural network (ANN) model, were developed and trained to analyze the relationship between approach volumes and turning movements. The models were evaluated and the ANN model showed the best performance. These models provide reliable and fast methods for estimating turning movements.