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

Traffic signal control based on a predicted traffic jam distribution

2009 ◽  
Vol 14 (2) ◽  
pp. 134-137 ◽  
Author(s):  
Cheng-You Cui ◽  
Ji-Sun Shin ◽  
Fumihiro Shoji ◽  
Hee-Hyol Lee
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

An Intelligent Traffic Signal Control System Based on ARM Cortex-M3

2014 ◽  
Vol 602-605 ◽  
pp. 1378-1382 ◽  
Author(s):  
Shan Ying Cheng ◽  
Xue Mei Zhou ◽  
Qin Jiang
Keyword(s):  
Control System ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam ◽  
Task Control ◽  
Embedded Rtos ◽  
Stable Performance ◽  
Population Genetic Algorithm ◽  
Result Analysis

In order to alleviate traffic jam, an intelligent traffic signal control system base on ARM Cortex-M3 is implemented. In the system, STM32F207 is processor. Embedded RTOS CoOS is transplanted to achieve multi-task control of traffic signal in software design. A new multi-population genetic algorithm is developed to optimize green ratio. The result analysis shows that the system has stable performance and it makes the optimization of green ratio convenient and swift.

scholarly journals Traffic Signal Control Based on Predicted Distribution of Traffic Jam

2009 ◽  
Vol 2009 (0) ◽  
pp. 49-52
Author(s):  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Ji-Sun Shin ◽  
Jin-Il Kim ◽  
Michio Miyazaki ◽  
...  
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

Real-Time Traffic Signal Control for Optimization of Traffic Jam Probability

2012 ◽  
Vol 132 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Cheng-you Cui ◽  
Ji-sun Shin ◽  
Michio Miyazaki ◽  
Hee-hyol Lee
Keyword(s):  
Real Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam ◽  
Real Time Traffic

Real-time traffic signal control for optimization of traffic jam probability

2012 ◽  
Vol 96 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Cheng-You Cui ◽  
Ji-Sun Shin ◽  
Michio Miyazaki ◽  
Hee-Hyol Lee
Keyword(s):  
Real Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam ◽  
Real Time Traffic

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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