scholarly journals Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenghua Zhang ◽  
Jin Qian ◽  
Chongxin Fang ◽  
Guoshu Liu ◽  
Quan Su
Keyword(s):  
Reinforcement Learning ◽  
Cooperative Game ◽  
Large Scale ◽  
Control Method ◽  
Transportation Network ◽  
Traffic Signal ◽  
Learning Ability ◽  
Hybrid Network ◽  
Traffic Signal Control ◽  
Communication And Coordination

In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its learning ability. The distributed multiagent RL (MARL) can avoid this kind of problem by observing some areas of each local RL in the complex plane traffic area. However, due to the limited communication capabilities between each agent, the environment becomes partially visible. This paper proposes multiagent reinforcement learning based on cooperative game (CG-MARL) to design the intersection as an agent structure. The method considers not only the communication and coordination between agents but also the game between agents. Each agent observes its own area to learn the RL strategy and value function, then concentrates the Q function from different agents through a hybrid network, and finally forms its own final Q function in the entire large-scale transportation network. The results show that the proposed method is superior to the traditional control method.

scholarly journals An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4291 ◽  
Author(s):  
Qiang Wu ◽  
Jianqing Wu ◽  
Jun Shen ◽  
Binbin Yong ◽  
Qingguo Zhou
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Communication Protocol ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Light Control ◽  
Multi Agent

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

scholarly journals Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Xiaoguang Yang ◽  
Yangdong Liu ◽  
Hailun Liang
Keyword(s):  
Reinforcement Learning ◽  
Hierarchical Control ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Learning Ability ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multidimensional Space ◽  
High Flows ◽  
Multiple Intersections

Reinforcement learning method has a self-learning ability in complex multidimensional space because it does not need accurate mathematical model and due to the low requirement for prior knowledge of the environment. The single intersection, arterial lines, and regional road network of a group of multiple intersections are taken as the research object on the paper. Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve the current signal timing scheme. However, the traffic signal optimization strategy based on reinforcement learning is suitable for complex traffic environments (high flows and multiple intersections), and the effects of which are better than the current optimization methods in the conditions of high flows in single intersections, arteries, and regional multi-intersection. In a word, the problem of insufficient traffic signal control capability is studied, and the hierarchical control algorithm based on reinforcement learning is applied to traffic signal control, so as to provide new ideas and methods for traffic signal control theory.

scholarly journals Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

2020 ◽  
Vol 34 (04) ◽  
pp. 3414-3421 ◽  
Author(s):  
Chacha Chen ◽  
Hua Wei ◽  
Nan Xu ◽  
Guanjie Zheng ◽  
Ming Yang ◽  
...  
Keyword(s):  
New York ◽  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Large Scale ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Urban Networks ◽  
Traffic Lights ◽  
Signal Coordination

Traffic congestion plagues cities around the world. Recent years have witnessed an unprecedented trend in applying reinforcement learning for traffic signal control. However, the primary challenge is to control and coordinate traffic lights in large-scale urban networks. No one has ever tested RL models on a network of more than a thousand traffic lights. In this paper, we tackle the problem of multi-intersection traffic signal control, especially for large-scale networks, based on RL techniques and transportation theories. This problem is quite difficult because there are challenges such as scalability, signal coordination, data feasibility, etc. To address these challenges, we (1) design our RL agents utilizing ‘pressure’ concept to achieve signal coordination in region-level; (2) show that implicit coordination could be achieved by individual control agents with well-crafted reward design thus reducing the dimensionality; and (3) conduct extensive experiments on multiple scenarios, including a real-world scenario with 2510 traffic lights in Manhattan, New York City 1 2.

scholarly journals Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 744 ◽  
Author(s):  
Song Wang ◽  
Xu Xie ◽  
Kedi Huang ◽  
Junjie Zeng ◽  
Zimin Cai
Keyword(s):  
Reinforcement Learning ◽  
High Resolution ◽  
Control Strategy ◽  
Control Method ◽  
Control Strategies ◽  
Fixed Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Event Based

Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies.

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