scholarly journals Autonomous Road Surveillance System: A Proposed Model for Vehicle Detection and Traffic Signal Control

2013 ◽  
Vol 19 ◽  
pp. 963-970 ◽  
Author(s):  
M.D. Hazrat Ali ◽  
Syuhei Kurokawa ◽  
A.A. Shafie
Keyword(s):  
Surveillance System ◽  
Vehicle Detection ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
System A ◽  
Proposed Model

scholarly journals Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Duowei Li ◽  
Jianping Wu ◽  
Ming Xu ◽  
Ziheng Wang ◽  
Kezhen Hu
Keyword(s):  
Reinforcement Learning ◽  
Waiting Time ◽  
Traffic Signals ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Average Waiting Time ◽  
Adaptive Traffic Signal Control ◽  
Proposed Model

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.

scholarly journals Using Reinforcement Learning With Partial Vehicle Detection for Intelligent Traffic Signal Control

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Rusheng Zhang ◽  
Akihiro Ishikawa ◽  
Wenli Wang ◽  
Benjamin Striner ◽  
Ozan K. Tonguz
Keyword(s):  
Reinforcement Learning ◽  
Vehicle Detection ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control

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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