[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 …
Multi-agent deep reinforcement learning for large-scale traffic signal control
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
Bioinspired computational intelligence and transportation systems: a long road ahead
This paper capitalizes on the increasingly high relevance gained by data-intensive
technologies in the development of intelligent transportation system, which calls for the …
technologies in the development of intelligent transportation system, which calls for the …
Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events
The transportation demand is rapidly growing in metropolises, resulting in chronic traffic
congestions in dense downtown areas. Adaptive traffic signal control as the principle part of …
congestions in dense downtown areas. Adaptive traffic signal control as the principle part of …
An intelligent IoT based traffic light management system: deep reinforcement learning
Traffic is one of the indispensable problems of modern societies, which leads to undesirable
consequences such as time wasting and greater possibility of accidents. Adaptive Traffic …
consequences such as time wasting and greater possibility of accidents. Adaptive Traffic …
An information fusion approach to intelligent traffic signal control using the joint methods of multiagent reinforcement learning and artificial intelligence of things
With the development of communication technology and artificial intelligence of things
(AIoT), transportation systems have become much smarter than ever before. However, the …
(AIoT), transportation systems have become much smarter than ever before. However, the …
[HTML][HTML] CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor
Tackling traffic signal control through multi-agent reinforcement learning is a widely-
employed approach. However, current state-of-the-art models have drawbacks: intersections …
employed approach. However, current state-of-the-art models have drawbacks: intersections …
Causal inference multi-agent reinforcement learning for traffic signal control
A primary challenge in multi-agent reinforcement learning for traffic signal control is to
produce effective cooperative traffic-signal policies in non-stationary multi-agent traffic …
produce effective cooperative traffic-signal policies in non-stationary multi-agent traffic …
Deep learning and case-based reasoning for predictive and adaptive traffic emergency management
An efficient traffic signal control system (TSCS) should not only be reactive to the current
traffic but also be predictive by anticipating future traffic disturbances. In this study, we …
traffic but also be predictive by anticipating future traffic disturbances. In this study, we …