Context-Aware Multiagent Broad Reinforcement Learning for Mixed Pedestrian-Vehicle Adaptive Traffic Light Control

Efficient traffic light control is a critical part of realizing smart transportation. In particular, deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have superior autonomous decision-making ability. Most existing work has applied DRL to control traffic lights intell...

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Bibliographic Details
Published in:IEEE internet of things journal 2022-10, Vol.9 (20), p.19694-19705
Main Authors: Zhu, Ruijie, Wu, Shuning, Li, Lulu, Lv, Ping, Xu, Mingliang
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Artificial neural networks
Broad reinforcement learning (BRL)
Context
context-aware
Decision making
Deep learning
deep reinforcement learning (DRL)
Internet of Things
Learning systems
Machine learning
multiagent
Multiagent systems
Pedestrians
Reinforcement learning
Roads
Smart cities
Smart transportation
Traffic congestion
Traffic control
traffic light control
Traffic signals
Training
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Summary:Efficient traffic light control is a critical part of realizing smart transportation. In particular, deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have superior autonomous decision-making ability. Most existing work has applied DRL to control traffic lights intelligently. In this article, we propose a novel context-aware multiagent broad reinforcement learning (CAMABRL) approach based on broad reinforcement learning (BRL) for mixed pedestrian-vehicle adaptive traffic light control (ATLC). CAMABRL exploits the broad learning system (BLS) established in a flat network structure to make decisions instead of a deep network structure. Unlike previous works that consider the attributes of vehicles, CAMABRL also takes the states of pedestrians waiting at the intersection into consideration. Combining with the context-aware mechanism that utilizes the states of adjacent agents and potential state information captured by the long short-term memory (LSTM) network, agents can make farsighted decisions to alleviate traffic congestion. The experimental results show that CAMABRL is superior to several state-of-the-art multiagent reinforcement learning (MARL) methods.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3167029