[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward
MJ Zideh, P Chatterjee, AK Srivastava - IEEE Access, 2023 - ieeexplore.ieee.org
Advancements in digital automation for smart grids have led to the installation of
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …
A new reinforcement learning-based variable speed limit control approach to improve traffic efficiency against freeway jam waves
Conventional reinforcement learning (RL) models of variable speed limit (VSL) control
systems (and traffic control systems in general) cannot be trained in real traffic process …
systems (and traffic control systems in general) cannot be trained in real traffic process …
Extending ramp metering control to mixed autonomy traffic flow with varying degrees of automation
The emergence of automated vehicles may have significant impacts on traffic flow. While
many studies suggest that fully automated vehicles can improve traffic flow by changing their …
many studies suggest that fully automated vehicles can improve traffic flow by changing their …
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
Deep learning methods are emerging as popular computational tools for solving forward
and inverse problems in traffic flow. In this paper, we study a neural operator framework for …
and inverse problems in traffic flow. In this paper, we study a neural operator framework for …
A survey on physics informed reinforcement learning: Review and open problems
The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …
many application areas. This involves enhancing the learning process by incorporating …
A multi-agent reinforcement learning-based longitudinal and lateral control of CAVs to improve traffic efficiency in a mandatory lane change scenario
Bottleneck areas are prone to severe traffic congestion due to the sudden drop in capacity.
To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep …
To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep …
[HTML][HTML] Human as AI mentor: Enhanced human-in-the-loop reinforcement learning for safe and efficient autonomous driving
Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
TD3LVSL: A lane-level variable speed limit approach based on twin delayed deep deterministic policy gradient in a connected automated vehicle environment
Variable speed limit (VSL) control plays a vital role in the emerging connected automated
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …
Coordinated control of urban expressway integrating adjacent signalized intersections using adversarial network based reinforcement learning method
This paper proposes an adversarial reinforcement learning (RL)-based traffic control
strategy to improve the traffic efficiency of an integrated network with expressway and …
strategy to improve the traffic efficiency of an integrated network with expressway and …