Quantitative Analysis of Vehicular Traffic Flow Order at Signalized Intersections
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 2
Abstract
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.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant 52122215 and the Shanghai Shuguang Program under Grant 22SG45.
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Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 2 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 3, 2023
Accepted: Sep 28, 2023
Published online: Nov 29, 2023
Published in print: Feb 1, 2024
Discussion open until: Apr 29, 2024
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