Traffic Signal Timing via Parallel Reinforcement Learning

2019 ◽  
pp. 113-123
Author(s):  
Qian Zhao ◽  
Cheng Xu ◽  
Sheng jin
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signal ◽  
Signal Timing ◽  
Traffic Signal Timing

scholarly journals Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing

2021 ◽  
Author(s):  
Areej Salaymeh ◽  
Loren Schwiebert ◽  
Stephen Remias
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signals ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Model Free ◽  
Proposed Model ◽  
Multi Agent ◽  
Traffic Signal Timing

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

scholarly journals Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Li ◽  
Lijie Yu ◽  
Siran Tao ◽  
Kuanmin Chen
Keyword(s):  
Simulation Software ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Simulation Environment ◽  
Multi Objective Optimization ◽  
Good Efficiency ◽  
Multi Objective ◽  
Traffic Signal Timing

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

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