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 …
[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 …
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 …
Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …
attention, due to higher demands for road safety and efficiency in highly interconnected road …
Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning
T Wang, J Cao, A Hussain - Transportation research part C: emerging …, 2021 - Elsevier
Recent research reveals that reinforcement learning can potentially perform optimal
decision-making compared to traditional methods like Adaptive Traffic Signal Control …
decision-making compared to traditional methods like Adaptive Traffic Signal Control …
Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle
An efficient energy split among different source of energy has been a challenge for existing
hybrid electric vehicle (HEV) supervisory control system. It requires an optimized energy use …
hybrid electric vehicle (HEV) supervisory control system. It requires an optimized energy use …
A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control
Abstract Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
Hierarchical traffic signal optimization using reinforcement learning and traffic prediction with long-short term memory
M Abdoos, ALC Bazzan - Expert systems with applications, 2021 - Elsevier
Multi-agent systems can be used for modelling large-scale distributed systems in real world
applications. In intelligent transportation system (ITS), many interacting entities influence the …
applications. In intelligent transportation system (ITS), many interacting entities influence the …
Traffic signal control for smart cities using reinforcement learning
Traffic congestion is increasing globally, and this problem needs to be addressed by the
traffic management system. Traffic signal control (TSC) is an effective method among various …
traffic management system. Traffic signal control (TSC) is an effective method among various …