Shubin Xie

Liang Zhang, Yutong Zhang, Shubin Xie et al.

Reinforcement learning (RL) is gaining popularity as an effective approach for traffic signal control (TSC) and is increasingly applied in this domain. However, most existing RL methodologies are confined to a single-stage TSC framework, primarily focusing on selecting an appropriate traffic signal phase at fixed action intervals, leading to inflexible and less adaptable phase durations. To address such limitations, we introduce a novel two-stage TSC framework named DynamicLight. This framework initiates with a phase control strategy responsible for determining the optimal traffic phase, followed by a duration control strategy tasked with determining the corresponding phase duration. Experimental results show that DynamicLight outperforms state-of-the-art TSC models and exhibits exceptional model generalization capabilities. Additionally, the robustness and potential for real-world implementation of DynamicLight are further demonstrated and validated through various DynamicLight variants. The code is released at https://github.com/LiangZhang1996/DynamicLight.

Liang Zhang, Shubin Xie, Jianming Deng

We would like to withdraw this article for the following reasons: 1 this article is not satisfactory for limited language and theoretical description; 2 we have enriched and revised this article with the help of other authors; 3 we must update the author contribution information.

Liang Zhang, Shubin Xie, Jianming Deng

Reinforcement learning (RL) techniques for traffic signal control (TSC) have gained increasing popularity in recent years. However, most existing RL-based TSC methods tend to focus primarily on the RL model structure while neglecting the significance of proper traffic state representation. Furthermore, some RL-based methods heavily rely on expert-designed traffic signal phase competition. In this paper, we present a novel approach to TSC that utilizes queue length as an efficient state representation. We propose two new methods: (1) Max Queue-Length (M-QL), an optimization-based traditional method designed based on the property of queue length; and (2) AttentionLight, an RL model that employs the self-attention mechanism to capture the signal phase correlation without requiring human knowledge of phase relationships. Comprehensive experiments on multiple real-world datasets demonstrate the effectiveness of our approach: (1) the M-QL method outperforms the latest RL-based methods; (2) AttentionLight achieves a new state-of-the-art performance; and (3) our results highlight the significance of proper state representation, which is as crucial as neural network design in TSC methods. Our findings have important implications for advancing the development of more effective and efficient TSC methods. Our code is released on Github (https://github. com/LiangZhang1996/AttentionLight).