Traffic signal optimization control method based on adaptive weighted averaged double deep Q network

Chen, Youqing; Zhang, Huizhen; Liu, Minglei; Ye, Ming; Xie, Hui

doi:10.1007/s10489-023-04469-9

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…In the study of signal timing optimization at intersections, Wang [1] promoted urban traffic signal control by the genetic timing scheduling model (GTSM) to, which has excellent performance in various scenarios of controlling traffic signals and accelerating urban traffic flow. Chen [2] proposed a traffic signal optimization control method based on adaptive weighted average double depth Q network (AWA-DDQN), which significantly improved the traffic flow efficiency at intersections. Wang [3] used electronic police equipment to directly calculate traffic signal timing settings through data, in order to solve the problem of not being able to obtain signal timing due to various reasons in practice.…”

Section: Introductionmentioning

confidence: 99%

Optimization strategy of signal timing at level crossing based on real-time queue length

Sun,

Shao,

Song

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Section: Introductionmentioning

confidence: 99%

Optimization strategy of signal timing at level crossing based on real-time queue length

Sun,

Shao,

Song

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…The model features an embedded selfattention mechanism that enables agents to adjust their attention in real time based on dynamic traffic flow information, facilitating more efficient cooperation between agents. Chen et al [25] developed the AWA-DDQN algorithm for traffic signal optimization, which dynamically tweaks parameters based on real-world interactions. While its adaptability is novel, its applicability in interconnected multi-intersection settings is yet to be tested and the algorithm's efficiency needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

Wang,

Xu,

2023

Sustainability

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To alleviate intersection traffic congestion and reduce carbon emissions at intersections, research on exploiting reinforcement learning for intersection signal control has become a frontier topic in the field of intelligent transportation. This study utilizes a deep reinforcement learning algorithm based on the D3QN (dueling double deep Q network) to achieve adaptive control of signal timings. Under a mixed traffic environment with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), this study constructs a reward function (Reward—CO2 Reduction) to minimize vehicle waiting time and carbon dioxide emissions at the intersection. Additionally, to account for the spatiotemporal distribution characteristics of traffic flow, an adaptive-phase action space and a fixed-phase action space are designed to optimize action selections. The proposed algorithm is validated in a SUMO simulation with different traffic volumes and CAV penetration rates. The experimental results are compared with other control strategies like Webster’s method (fixed-time control). The analysis shows that the proposed model can effectively reduce carbon dioxide emissions when the traffic volume is low or medium. As the penetration rate of CAVs increases, the average carbon dioxide emissions and waiting time can be further reduced with the proposed model. The significance of this study lies in its dual achievement: by presenting a flexible strategy that not only reduces the environmental impact by lowering carbon dioxide emissions but also enhances traffic efficiency, it provides a tangible example of the advancement of green intelligent transportation systems.

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A Distributed Multi Head Policy Traffic Signal Control Method Based on Cluster Sampling for Efficient Training

Yu,

Mi,

Han

et al. 2024

Lecture Notes in Civil Engineering

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Traffic signal optimization control method based on adaptive weighted averaged double deep Q network

Cited by 7 publications

References 44 publications

Optimization strategy of signal timing at level crossing based on real-time queue length

Optimization strategy of signal timing at level crossing based on real-time queue length

Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

A Distributed Multi Head Policy Traffic Signal Control Method Based on Cluster Sampling for Efficient Training

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