A case study for traffic signal control system evaluation based on artificial transportation systems

2011 ◽  
Author(s):  
Fenghua Zhu ◽  
Zhenjiang Li
Keyword(s):  
Control System ◽  
Transportation Systems ◽  
Traffic Signal ◽  
System Evaluation ◽  
Signal Control ◽  
Traffic Signal Control

scholarly journals An Adaptive Signal Control Method with Optimal Detector Locations

2019 ◽  
Vol 11 (3) ◽  
pp. 727 ◽  
Author(s):  
Senlai Zhu ◽  
Ke Guo ◽  
Yuntao Guo ◽  
Huairen Tao ◽  
Quan Shi
Keyword(s):  
Control System ◽  
Control Method ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Primary Role ◽  
Adaptive Traffic Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

The adaptive traffic signal control system is a key component of intelligent transportation systems and has a primary role in effectively reducing traffic congestion. The high costs of implementation and maintenance limit the applicability of the adaptive traffic signal control system, especially in developing countries. This paper proposes a low-cost adaptive signal control method that is easy to implement. Two detectors are installed in each vehicle lane at an optimal location determined by the proposed method to detect green and red redundancy time, based on which the original signal timing is adjusted through a signal controller. The proposed method is evaluated through case studies with low and high volume-to-capacity ratio intersections. The results show that the proposed adaptive signal control method can significantly reduce total traffic delay at intersections.

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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