scholarly journals Q-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network

2012 ◽  
Author(s):  
Yit Kwong Chin ◽  
Wei Yeang Kow ◽  
Wei Leong Khong ◽  
Min Keng Tan ◽  
Kenneth Tze Kin Teo
Keyword(s):  
Traffic Signal ◽  
Traffic Network ◽  
Signal Optimization ◽  
Q Learning ◽  
Multiple Intersections

scholarly journals Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10688
Author(s):  
Jaun Gu ◽  
Minhyuck Lee ◽  
Chulmin Jun ◽  
Yohee Han ◽  
Youngchan Kim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Optimization ◽  
Fixed Time ◽  
Traffic Signal ◽  
Real Field ◽  
Urban Mobility ◽  
Signal Optimization ◽  
Time Model ◽  
Proposed Model ◽  
Multiple Intersections

In order to deal with dynamic traffic flow, adaptive traffic signal controls using reinforcement learning are being studied. However, most of the related studies are difficult to apply to the real field considering only mathematical optimization. In this study, we propose a reinforcement learning-based signal optimization model with constraints. The proposed model maintains the sequence of typical signal phases and considers the minimum green time. The model was trained using Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The model was evaluated in the virtual environment similar to a real road with multiple intersections connected. The performance of the proposed model was analyzed by comparing the delay and number of stops with a reinforcement learning model that did not consider constraints and a fixed-time model. In a peak hour, the proposed model reduced the delay from 3 min 15 s to 2 min 15 s and the number of stops from 11 to 4.7 compared to the fixed-time model.

Solving Traffic Signal Scheduling Problems in Heterogeneous Traffic Network by Using Meta-Heuristics

2019 ◽  
Vol 20 (9) ◽  
pp. 3272-3282 ◽  
Author(s):  
Kaizhou Gao ◽  
Yicheng Zhang ◽  
Rong Su ◽  
Fajun Yang ◽  
Ponnuthurai Nagaratnam Suganthan ◽  
...  
Keyword(s):  
Traffic Signal ◽  
Heterogeneous Traffic ◽  
Traffic Network ◽  
Scheduling Problems

scholarly journals Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Li Wang ◽  
Xiao-ming Liu ◽  
Zhi-jian Wang ◽  
Zheng-xi Li
Keyword(s):  
Control System ◽  
Network Analysis ◽  
Traffic Control ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Analysis Approach ◽  
System Control ◽  
Traffic Network ◽  
Operation Performance ◽  
Traffic Control System

Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

scholarly journals IoT based smart traffic signal monitoring system using vehicles counts

2018 ◽  
Vol 7 (2.21) ◽  
pp. 309 ◽  
Author(s):  
Senthil Kumar Janahan ◽  
M R.M. Veeramanickam ◽  
S Arun ◽  
Kumar Narayanan ◽  
R Anandan ◽  
...  
Keyword(s):  
Waiting Time ◽  
Clustering Algorithms ◽  
Traffic Signal ◽  
Traffic Network ◽  
The Road ◽  
Real Time Traffic ◽  
Proposed Model ◽  
Timing Interval ◽  
On The Road ◽  
Smart Traffic

Traffic signal management is one of the major problematic issues in the current situation. Such scenarios, every signal are getting 60 seconds of timing on the road at a regular interval, even when traffic on that particular road is dense. As per this proposed model in this article, which will be optimized the timing interval of the traffic signal purely depends on the number of vehicles on that particular roadside. The major advantage of this system is that it can able to decrease the more waiting time for the drivers to cross road signal.  In this model, we are using the clustering algorithms model which is based on KNN algorithm. Using this algorithm new model will be liable to determine expected required timing as per provided inputs to the signal which is vehicles count. The input of these systems is vehicles counts on each side of the road from crossing signal.  And this input will be determined on much time is to be provided. “Case studies on this system are traffic network and real-time traffic sub-networks are organized to get the effectiveness of the proposed model.”  

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