A review on computational intelligence methods for controlling traffic signal timing
S Araghi, A Khosravi, D Creighton - Expert systems with applications, 2015 - Elsevier
Urban traffic as one of the most important challenges in modern city life needs practically
effective and efficient solutions. Artificial intelligence methods have gained popularity for …
effective and efficient solutions. Artificial intelligence methods have gained popularity for …
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 …
Traffic light control in non-stationary environments based on multi agent Q-learning
M Abdoos, N Mozayani… - 2011 14th International …, 2011 - ieeexplore.ieee.org
In many urban areas where traffic congestion does not have the peak pattern, conventional
traffic signal timing methods does not result in an efficient control. One alternative is to let …
traffic signal timing methods does not result in an efficient control. One alternative is to let …
Holonic multi-agent system for traffic signals control
Agent-based technologies are rapidly growing as a powerful tool for modelling and
developing large-scale distributed systems. Recently, multi-agent systems are largely used …
developing large-scale distributed systems. Recently, multi-agent systems are largely used …
Meta-learning based spatial-temporal graph attention network for traffic signal control
Traffic signal control is of great importance to the urban transportation systems and public
travel, yet it becomes challenging because of two essential factors. First, spatial–temporal …
travel, yet it becomes challenging because of two essential factors. First, spatial–temporal …
Dealing with non-stationary environments using context detection
BC Da Silva, EW Basso, ALC Bazzan… - Proceedings of the 23rd …, 2006 - dl.acm.org
In this paper we introduce RL-CD, a method for solving reinforcement learning problems in
non-stationary environments. The method is based on a mechanism for creating, updating …
non-stationary environments. The method is based on a mechanism for creating, updating …
Dynstgat: Dynamic spatial-temporal graph attention network for traffic signal control
Adaptive traffic signal control plays a significant role in the construction of smart cities. This
task is challenging because of many essential factors, such as cooperation among …
task is challenging because of many essential factors, such as cooperation among …
Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning
ARM Jamil, KK Ganguly… - IET Intelligent Transport …, 2020 - Wiley Online Library
The increasing traffic congestion problem can be solved by an adaptive traffic signal control
(ATSC) system as it utilises real‐time traffic information to control traffic signals. Recently …
(ATSC) system as it utilises real‐time traffic information to control traffic signals. Recently …
Hierarchical control of traffic signals using Q-learning with tile coding
Multi-agent systems are rapidly growing as powerful tools for Intelligent Transportation
Systems (ITS). It is desirable that traffic signals control, as a part of ITS, is performed in a …
Systems (ITS). It is desirable that traffic signals control, as a part of ITS, is performed in a …
Intelligent traffic light control of isolated intersections using machine learning methods
S Araghi, A Khosravi, M Johnstone… - … on Systems, Man, and …, 2013 - ieeexplore.ieee.org
Traffic congestion is one of the major problems in modern cities. This study applies machine
learning methods to determine green times in order to minimize in an isolated intersection. Q …
learning methods to determine green times in order to minimize in an isolated intersection. Q …