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Aug 12, 2020
Optimization of Two-Way Green Wave Band Control Based on the Calibration of the Stopping Cars
Authors: Mengmeng Zhang [email protected], Tingting Sun [email protected], Chunting Nie [email protected], and Di Wu [email protected]Author Affiliations
Publication: CICTP 2020
ABSTRACT
Optimization of two-way green waveband control for urban artery is beneficial to reducing the vehicle delay, queue length, waiting time, and the number of stops. And then, it can alleviate traffic congestion and improve using-efficiency of roads. Establish a more general control optimization model based on the departure of queuing vehicles at the downstream intersection, the arrival of vehicles from the upstream intersection to the downstream intersection, and the situation when the vehicle M meets the red light at the intersection. This control optimization model overcomes the limitations of existing models which only consider the vehicles which the head or tail of the fleet meets red light at the downstream intersection. However, this control optimization model considers the actual number of vehicles which meet red traffic light at the intersection. The objective of optimization control is the two-way vehicles delay. Genetic algorithm is used to calculate the phase difference in the optimization model. Taking the intersection along the lean road in Dongying City as an example. There is a simulation of the control optimization model by genetic algorithm of MATLAB, and the simulation results were evaluated by average delay. Compared with actual data, the result shows that the average delay reduced 41.8%. The control optimization model is effective.
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© 2020 American Society of Civil Engineers.
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Published online: Aug 12, 2020
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1Engineering College of Transport and Logistics, Shandong Jiaotong Univ., P.O. Box 250357, Jinan, China. Email: [email protected]
2Marine College, Ningbo Univ., P.O. Box 315832, Ningbo, China. Email: [email protected]
3Key Lab of Transportation Engineering of Beijing, Beijing Univ. of Technology, P.O. Box 100124, Beijing, China. Email: [email protected]
4College of Traffic and Transportation, Chongqing Jiaotong Univ., P.O. Box 400074, Chongqing, China. Email: [email protected]
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