Evaluating Action Durations for Adaptive Traffic Signal Control Based On Deep Q-Learning

Çeltek, Seyit Alperen; Durdu, Akif; Ali, Muslim

doi:10.1007/s13177-021-00262-5

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…In order to enhance the prediction ability of deep learning, ensemble methods are widely used in load forecasting [15][16][17][18][19][20][21][22][23][24][25]. The ensemble model can improve the predictive capability of the model by combining multiple predictors [26].…”

Section: Introductionmentioning

confidence: 99%

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

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Short‐term load forecasting is critical for power system planning and operations, and ensemble forecasting methods for electricity loads have been shown to be effective in obtaining accurate forecasts. However, the weights in ensemble prediction models are usually preset based on the overall performance after training, which prevents the model from adapting in the face of different scenarios, limiting the improvement of prediction performance. In order to improve the accurateness and validity of the ensemble prediction method further, this paper proposes an ensemble deep reinforcement learning approach using Q‐learning dynamic weight assignment to consider local behaviours caused by changes in the external environment. Firstly, the variational mode decomposition is used to reduce the non‐stationarity of the original data by decomposing the load sequence. Then, the recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU) are selected as the basic power load predictors. Finally, the optimal weights are ensembled for the three sub‐predictors by the optimal weights generated using the Q‐learning algorithm, and the final results are obtained by combining their respective predictions. The results show that the forecasting capability of the proposed method outperforms all sub‐models and several baseline ensemble forecasting methods.

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Section: Introductionmentioning

confidence: 99%

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

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Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Fu,

Zhang,

Tao

et al. 2024

Meas. Sci. Technol.

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The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

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Deep Reinforcement Learning Approach for Smart Traffic Signal Control System

Sujihelen,

Senthilsingh,

Asha

et al. 2024

2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)

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Evaluating Action Durations for Adaptive Traffic Signal Control Based On Deep Q-Learning

Cited by 6 publications

References 39 publications

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Deep Reinforcement Learning Approach for Smart Traffic Signal Control System

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