Research on Integrated Control Strategy for Highway Merging Bottlenecks Based on Collaborative Multi-Agent Reinforcement Learning
The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores...
Saved in:
Main Authors: | Juan Du, Anshuang Yu, Hao Zhou, Qianli Jiang, Xueying Bai |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/2/836 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Design and Prototype of an Automatic Highway Streetlight Intensity Control System.
by: Wafula, Moses
Published: (2024) -
Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
by: Ju-Hwan Lee, et al.
Published: (2025-01-01) -
Do so replacement and the argument/adjunct distinction in Merge-based syntax
by: Yuji Shuhama
Published: (2022-12-01) -
Lane Changing Control of Autonomous Vehicle With Integrated Trajectory Planning Based on Stackelberg Game
by: Dongmei Wu, et al.
Published: (2024-01-01) -
IALight: Importance-Aware Multi-Agent Reinforcement Learning for Arterial Traffic Cooperative Control
by: Lu WEI, et al.
Published: (2025-02-01)