Double Deep Q-Network with a Dual-Agent for Traffic Signal Control

Gu, Jimmy; Fang, Yong; Sheng, Zhiqiang; Wen, Peng

doi:10.3390/app10051622

Cited by 31 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparison was performed based on the simulation model and the obtained results indicate the superiority of the algorithm as compared to a fixed time and the longest queue first algorithm. In [25] the four-phase isolated intersection adaptive traffic signal control based on the double DQN (DDQN) is proposed. Fixed phase sequence stable traffic signal control policy is achieved using dual agent architecture.…”

Section: The Related Workmentioning

confidence: 99%

An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using SUMO Traffic Simulator

et al. 2021

View full text Add to dashboard Cite

In the past, the Webster optimal cycle time formula was limited to calculate the optimal cycle from historical data for fixed-time traffic signal control. This paper focuses on the design of an adaptive traffic signal control based on fuzzy logic with Webster and modified Webster's formula. These formulas are used to calculate the optimal cycle time depending on the current traffic situation which applying in the next cycle. The alternation of the traffic condition between two successive cycles is monitored and handled through the fuzzy logic system to compensate the fluctuation. The obtained optimal cycle time is used to determine adaptively the effective phase green times i.e. is used to determine adaptively the maximum allowable extension limit of the green phase in the next cycle. The SUMO traffic simulator is used to compare the results of the proposed adaptive control methods with fuzzy logic based traffic control, and a fixed-time Webster and modified Webster based traffic control methods. The proposed methods are tested on an isolated intersection. In this study, real field-collected data obtained from three, four, and five approaches intersections in Kilis/Turkey are used to test the performance of the proposed methods. In addition, to examine the efficiency of the proposed techniques at heavy demands, the arbitrary demands are generated by SUMO for a four approaches intersection. The obtained simulation results indicate that the proposed methods over perform the fixed time and fuzzy logic based traffic control methods in terms of average vehicular delay, speed, and travel time.INDEX TERMS Fuzzy logic control, Webster's formula, modified Webster's formula, adaptive traffic control, fixed-time traffic control, SUMO simulator.

show abstract

Section: The Related Workmentioning

confidence: 99%

An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using SUMO Traffic Simulator

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In [27], the authors proposed using a knowledge exchange protocol between agents to increase the level of cooperation between agents and achieve an optimal traffic light control strategy. A double Q-learning algorithm for improving the stability of control policy was investigated in [28]. In [29], the authors combined the recurrent neural network (RNN) with Deep Q-Network and showed that the proposed approach performs better in partially observed environment.…”

Section: Related Workmentioning

confidence: 99%

Traffic Signal Control: a Double Q-learning Approach

Agafonov¹,

Myasnikov²

2021

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Currently, the use of information and communication technologies for solving economic, social, transportation, and other problems in the urban environment is usually considered within the "smart city" concept. Optimal traffic management and, in particular, traffic signal control is one of the key components of smart cities. In this paper, we investigate the reinforcement learning approach, namely, the double Q-learning approach, to solve the traffic signal control problem. Both the initial data on the connected vehicles distribution and the aggregated characteristics of traffic flows are used to describe the state of the reinforcement learning agent. Experimental studies of the proposed model were carried out on synthetic and real data using the CityFlow microscopic traffic simulator.

show abstract

“…The route of each car is generated by the path generator randomly. The method by which vehicles are generated and departed into the network significantly impacts any traffic simulation quality [26]. The most popular vehicle production method is to randomly sample from probability distribution numbers that respect to the time interval between vehicles.…”

Section: A Experimental Settingsmentioning

confidence: 99%

A Coordination Algorithm for Signalized Multi-Intersection to Maximize Green Wave Band in V2X Network

Shi

Han

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The signal control system for multi-intersection traffic management is an extensive complex nonlinear and non-equilibrium system and usually leads to a weak control strategy. Existing studies of traffic dynamics mainly focus on the applications to the single network, whereas how the structure of multiple intersections affects the dynamical behaviors is unclear. Connected vehicles in recent studies have shown the potential to eliminate traffic communication barriers and balance the traffic load. For this consideration, we propose a new multi-intersection model based on Vehicle-to-Everything (V2X) network to capture the features of traffic flow, which employs vehicular communication to obtain real-time traffic information and share it among the adjacent intersections. Focus on smoothing traffic flow without stopand-go driving and improving the coordination of multiple traffic lights, we develop a multi-intersection coordination algorithm based on V2X (MICA-V for short) to maximize the green waveband in the arterial road. Due to the establishment of registration list yielding the average saturated headway time, our method is of high efficiency-the distributed process of data makes the convergence to be very fast with nearly linear time. Simulation results via MATLAB with Simulation of Urban Mobility (SUMO) showed the effectiveness of the proposed MICA-V in improving traffic capacity, reducing the number of stop behaviors, and improve the average speed of vehicles moving on the arterial road.

show abstract

Double Deep Q-Network with a Dual-Agent for Traffic Signal Control

Cited by 31 publications

References 21 publications

An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using SUMO Traffic Simulator

An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using SUMO Traffic Simulator

Traffic Signal Control: a Double Q-learning Approach

A Coordination Algorithm for Signalized Multi-Intersection to Maximize Green Wave Band in V2X Network

Contact Info

Product

Resources

About