Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

Anton Agafonov (), Alexander Yumaganov and Vladislav Myasnikov
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Anton Agafonov: Department of Geoinformatics and Information Security, Samara National Research University, 443086 Samara, Russia
Alexander Yumaganov: Department of Geoinformatics and Information Security, Samara National Research University, 443086 Samara, Russia
Vladislav Myasnikov: Department of Geoinformatics and Information Security, Samara National Research University, 443086 Samara, Russia

Mathematics, 2023, vol. 11, issue 6, 1-19

Abstract: Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods.

Keywords: adaptive traffic signal control; connected and automated vehicle; trajectory control; cooperative control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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