Deep reinforcement learning for traffic signal control: A review
Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas
worldwide. The integration of the newly emerging deep learning approach and the …
worldwide. The integration of the newly emerging deep learning approach and the …
Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data
Persistent congestions which are varying in strength and duration in the dense traffic
networks are the most prominent obstacle towards sustainable mobility. Those types of …
networks are the most prominent obstacle towards sustainable mobility. Those types of …
[HTML][HTML] Deep reinforcement learning for traffic signal control with consistent state and reward design approach
Abstract Intelligent Transportation Systems are essential due to the increased number of
traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a …
traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a …
[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 …
Traffic signal control using hybrid action space deep reinforcement learning
Recent research works on intelligent traffic signal control (TSC) have been mainly focused
on leveraging deep reinforcement learning (DRL) due to its proven capability and …
on leveraging deep reinforcement learning (DRL) due to its proven capability and …
A comparison of deep reinforcement learning models for isolated traffic signal control
Traditional control methods may not be adaptive enough for ever-changing traffic dynamics.
Hence, extensive deep reinforcement learning (DRL) methods have been utilized to solve …
Hence, extensive deep reinforcement learning (DRL) methods have been utilized to solve …
Hierarchically and cooperatively learning traffic signal control
Deep reinforcement learning (RL) has been applied to traffic signal control recently and
demonstrated superior performance to conventional control methods. However, there are …
demonstrated superior performance to conventional control methods. However, there are …
Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
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 …
Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway city, Malaysia
In most urban areas, traffic congestion is a vexing, complex and growing issue day by day.
Reinforcement learning (RL) enables a single decision maker (or an agent) to learn and …
Reinforcement learning (RL) enables a single decision maker (or an agent) to learn and …