scholarly journals Evolvable traffic signal control for intersection congestion alleviation with enhanced particle swarm optimisation

2017 ◽  
Author(s):  
Helen Sin Ee Chuo ◽  
Min Keng Tan ◽  
Alex Chee Hoe Chong ◽  
Renee Ka Yin Chin ◽  
Kenneth Tze Kin Teo
Keyword(s):  
Particle Swarm ◽  
Traffic Signal ◽  
Particle Swarm Optimisation ◽  
Signal Control ◽  
Traffic Signal Control

scholarly journals A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 280
Author(s):  
Yanjun Shi ◽  
Yuhan Qi ◽  
Lingling Lv ◽  
Donglin Liang
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Particle Swarm ◽  
Traffic Signal ◽  
Particle Swarm Optimisation ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Interval Model ◽  
Real Time Traffic

Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.

Stochastic Model of Traffic Jam and Traffic Signal Control

2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee
Keyword(s):  
Stochastic Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

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