Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

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 co...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 6; p. 1540
Main Authors: Agafonov, Anton, Yumaganov, Alexander, Myasnikov, Vladislav
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2023
Subjects:
Adaptive algorithms
Adaptive control
adaptive traffic signal control
Air pollution
Artificial neural networks
Autonomous vehicles
Communication
Computer simulation
connected and automated vehicle
Connectivity
Control
Control algorithms
Control methods
Control theory
Cooperative control
Efficiency
Energy consumption
Flow control
Fuel consumption
GDP
Gross Domestic Product
Infrastructure
Intelligent vehicle-highway systems
Interchanges and intersections
Internet of Things
Mathematics
Methods
Neural networks
Optimization
Predictive control
R&D
Research & development
Roads
Sensors
Simulation
Smart cities
Traffic accidents & safety
Traffic congestion
Traffic control
Traffic flow
Traffic signals
Traffic signs and signals
Trajectory control
Transportation systems
Travel time
Russia
United Kingdom > UK
United States > US
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Description
Summary: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11061540