4.7 Article

Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

期刊

MATHEMATICS
卷 11, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/math11061540

关键词

adaptive traffic signal control; connected and automated vehicle; trajectory control; cooperative control

向作者/读者索取更多资源

Cooperative control of vehicle trajectories and traffic signal phases is proposed to improve transportation system efficiency and safety. A cooperative control method is proposed, which combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. Numerical experiments show that the proposed method can reduce fuel consumption by 1% to 4.2%, travel time by 1% to 5.3%, and stop delays by 27%, compared to baseline methods, in different simulation scenarios.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据