Abstract
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.
Highlights
Traffic congestion has caused a series of severe negative impacts like longer waiting time, more gas cost, and severe air pollution
We present an auto communication protocol (ACP) between agents in multi-agent reinforcement learning (MARL) based on attention mechanism; We propose a multi-agent auto communication (MAAC) algorithm based on MARL and ACP in traffic light control; We build a practicable edge computing architecture for industrial deployment on Internet of Things (IoT), considering the limitations of the capabilities of network transmission bandwidth; The experiments show the MAAC framework outperformed 17 %over baseline models
We tested the algorithms in 500 episodes after the training process
Summary
Traffic congestion has caused a series of severe negative impacts like longer waiting time, more gas cost, and severe air pollution. According to a report in 2014 [1], the loss caused by traffic jams is up to $124 billion US dollars a year in the US. The shortage of traffic infrastructures, the growing number of vehicles, and the inefficient traffic signal control are key underlying reasons for traffic congestion. The traffic light control problem seems to be the most solved. The internal operation of the real urban transportation environment cannot be accurately calculated and analyzed mathematically due to its complexity and uncertainty. Reinforcement learning (RL), which is characterized by being data-driven, mode-less, and self-learning, is well suited for conducting research on adaptive traffic light control algorithms [2,3,4]
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