scholarly journals Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

2010 ◽  
Vol 02 (08) ◽  
pp. 483-486 ◽  
Author(s):  
Lin Dong ◽  
Wushan Chen
Keyword(s):  
Real Time ◽  
Traffic Signal ◽  
Signal Timing ◽  
Urban Road ◽  
Real Time Traffic ◽  
Traffic Signal Timing

scholarly journals A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoke Zhou ◽  
Fei Zhu ◽  
Quan Liu ◽  
Yuchen Fu ◽  
Wei Huang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Learning Algorithm ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Timing ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Signal Timing

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

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

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

2021 ◽  
pp. 036119812110119
Author(s):  
Yi Wang ◽  
Zhihong Yao ◽  
Yang Cheng ◽  
Yangsheng Jiang ◽  
Bin Ran
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Queue Length ◽  
Penetration Rate ◽  
Traffic Signal ◽  
Signal Timing ◽  
Connected Vehicle ◽  
Filtering Method ◽  
Length Estimation ◽  
Traffic Signal Timing

Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.

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