Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation
Traffic signal control is an important and challenging real-world problem that has recently
received a large amount of interest from both transportation and computer science …
received a large amount of interest from both transportation and computer science …
Transfer learning in deep reinforcement learning: A survey
Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …
problems. Recent years have witnessed remarkable progress in reinforcement learning …
Mobility digital twin: Concept, architecture, case study, and future challenges
A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging
technology attracted extensive attention from different industries during the past decade …
technology attracted extensive attention from different industries during the past decade …
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 …
Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Traffic congestion plagues cities around the world. Recent years have witnessed an
unprecedented trend in applying reinforcement learning for traffic signal control. However …
unprecedented trend in applying reinforcement learning for traffic signal control. However …
Traffic flow forecasting with spatial-temporal graph diffusion network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …
spatial-temporal mining applications, such as intelligent traffic control and public risk …
Presslight: Learning max pressure control to coordinate traffic signals in arterial network
Traffic signal control is essential for transportation efficiency in road networks. It has been a
challenging problem because of the complexity in traffic dynamics. Conventional …
challenging problem because of the complexity in traffic dynamics. Conventional …
Colight: Learning network-level cooperation for traffic signal control
Cooperation among the traffic signals enables vehicles to move through intersections more
quickly. Conventional transportation approaches implement cooperation by pre-calculating …
quickly. Conventional transportation approaches implement cooperation by pre-calculating …
[HTML][HTML] Reinforcement learning in urban network traffic signal control: A systematic literature review
Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
urban transportation and enhanced quality of life. Recently, the use of reinforcement …