Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy
In advanced transportation-management systems, variable speed limits are a crucial
application. Deep reinforcement learning methods have been shown to have superior …
application. Deep reinforcement learning methods have been shown to have superior …
Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm
Variable speed limit (VSL) control is a flexible way to improve traffic conditions, increase
safety, and reduce emissions. There is an emerging trend of using reinforcement learning …
safety, and reduce emissions. There is an emerging trend of using reinforcement learning …
Differential variable speed limits control for freeway recurrent bottlenecks via deep reinforcement learning
Variable speed limits (VSL) control is a flexible way to improve traffic condition, increase
safety and reduce emission. There is an emerging trend of using reinforcement learning …
safety and reduce emission. There is an emerging trend of using reinforcement learning …
DVS-RG: Differential Variable Speed Limits Control using Deep Reinforcement Learning with Graph State Representation
Variable speed limit (VSL) control is an established yet challenging problem to improve
freeway traffic mobility and alleviate bottlenecks by customizing speed limits at proper …
freeway traffic mobility and alleviate bottlenecks by customizing speed limits at proper …
Enhancing transferability of deep reinforcement learning-based variable speed limit control using transfer learning
The study aims to evaluate the performance of the transfer learning algorithm to enhance the
transferability of a deep reinforcement learning-based variable speed limits (VSL) control …
transferability of a deep reinforcement learning-based variable speed limits (VSL) control …
Integrated traffic control for freeway recurrent bottleneck based on deep reinforcement learning
C Wang, Y Xu, J Zhang, B Ran - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recent advances in deep reinforcement learning have shown promising results in solving
sophisticated control problems with high dimensional states and action space. Inspired by …
sophisticated control problems with high dimensional states and action space. Inspired by …
Multi-Agent Deep Reinforcement Learning for Multi-Lane Freeways Differential Variable Speed Limit Control in Mixed Traffic Environment
L Han, L Zhang, W Guo - Transportation Research Record, 2024 - journals.sagepub.com
In advanced freeway traffic management systems, variable speed limit control (VSLC) is
frequently discussed as one of the control measures. However, in a mixed traffic …
frequently discussed as one of the control measures. However, in a mixed traffic …
Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
The application of deep reinforcement learning (DRL) techniques in intelligent transportation
systems garners significant attention. In this field, reward function design is a crucial factor …
systems garners significant attention. In this field, reward function design is a crucial factor …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Flexible Traffic Signal Control via Multi-objective Reinforcement Learning
T Saiki, S Arai - IEEE Access, 2023 - ieeexplore.ieee.org
Deep reinforcement learning has been extensively studied for traffic signal control owing to
its ability to process large amounts of information and achieving superior performance …
its ability to process large amounts of information and achieving superior performance …