Robust Interpolating Traffic Signal Control for Uncertain Road Networks

2019 ◽  
Author(s):  
Shimon Komarovsky ◽  
Jack Haddad
Keyword(s):  
Road Networks ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control

A MILP-based MPC Method for Traffic Signal Control of Urban Road Networks *

2019 ◽  
Author(s):  
Bao-Lin Ye ◽  
Huimin Gao ◽  
Lingxi Li ◽  
Keyu Ruan ◽  
Weimin Wu ◽  
...  
Keyword(s):  
Road Networks ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Urban Road

scholarly journals A Hybrid Strategy for Real-Time Traffic Signal Control of Urban Road Networks

2011 ◽  
Vol 12 (3) ◽  
pp. 884-894 ◽  
Author(s):  
Anastasios Kouvelas ◽  
Konstantinos Aboudolas ◽  
Markos Papageorgiou ◽  
Elias B. Kosmatopoulos
Keyword(s):  
Real Time ◽  
Road Networks ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Hybrid Strategy ◽  
Urban Road ◽  
Real Time Traffic

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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