Real-time control and optimization of traffic signal timing transition for emergency vehicle preemption

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.

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Control strategies of traffic signal timing transition for emergency vehicle preemption

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2012.04.004 ◽

2012 ◽

Vol 25 ◽

pp. 1-17 ◽

Cited By ~ 37

Author(s):

Xiaolin Qin ◽

Ata M. Khan

Keyword(s):

Control Strategies ◽

Traffic Signal ◽

Signal Timing ◽

Emergency Vehicle ◽

Traffic Signal Timing

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211011996 ◽

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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A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

Methods of Information in Medicine ◽

10.1055/s-0038-1634619 ◽

1995 ◽

Vol 34 (05) ◽

pp. 475-488

Author(s):

B. Seroussi ◽

J. F. Boisvieux ◽

V. Morice

Keyword(s):

Real Time ◽

Linear Time ◽

Treatment Plan ◽

Control Architecture ◽

Real Time Control ◽

Time Representation ◽

Time Control ◽

Continuous Support ◽

Definition Of ◽

Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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