Under efficiency improvement of road networks by utilizing advanced traffic signal control methods, intelligent transportation systems intend to characterize a smart city. Recently, due to significant progress in artificial intelligence, machine learning-based framework of adaptive traffic signal control has been highly concentrated. In particular, deep Q-learning neural network is a model-free technique and can be applied to optimal action selection problems. However, setting variable green time is a key mechanism to reflect traffic fluctuations such that time steps need not be fixed intervals in reinforcement learning framework. In this study, the authors proposed a dynamic discount factor embedded in the iterative Bellman equation to prevent from a biased estimation of action-value function due to the effects of inconstant time step interval. Moreover, action is added to the input layer of the neural network in the training process, and the output layer is the estimated action-value for the denoted action. Then, the trained neural network can be used to generate action that leads to an optimal estimated value within a finite set as the agents' policy. The preliminary results show that the trained agent outperforms a fixed timing plan in all testing cases with reducing system total delay by 20%..
Value-based deep reinforcement learning for adaptive isolated intersection signal control
IET Intelligent Transport Systems ; 12 , 9 ; 1005-1010
20.08.2018
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
iterative Bellman equation , machine learning-based framework , dynamic programming , learning (artificial intelligence) , adaptive isolated intersection signal control , smart city , modern city , variable green time , neural nets , optimal discrete-time action selection problems , deep Q-learning neural network , traffic fluctuations , road traffic , model-free technique , traffic arrival rates , dynamic discount factor , traffic arrival patterns , VISSIM software , value-based deep reinforcement learning , intelligent transportation systems , road network efficiency improvement , advanced traffic signal control methods , artificial intelligence , iterative methods , biased action-value function estimation , traffic engineering computing
Metadata by IET is licensed under CC BY 3.0
Value‐based deep reinforcement learning for adaptive isolated intersection signal control
Wiley | 2018
|Urban intersection signal control method based on deep reinforcement learning
Europäisches Patentamt | 2024
|Multi-intersection signal cooperative control method based on deep reinforcement learning
Europäisches Patentamt | 2025
|Multi-intersection traffic signal control method based on deep reinforcement learning
Europäisches Patentamt | 2024
|