This video demonstration contrasts two approaches to coordination in traffic light control using reinforcement learning: earlier work, based on a deconstruction of the state space into a linear combination of vehicle states, and our own approach based on the Deep Q-learning algorithm
The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future ...
Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in ...
Due to the increasing amounts of traffic in and around urban areas there is a growing need for intel...
The aim of this thesis was to explore the possibilities of using deep reinforcement learning in traf...
The current traffic light controls are ineffective and causes a handful of problems such as congesti...
Recent advances in combining deep neural network architectures with reinforcement learning (RL) tech...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
Identifying the most efficient exploration approach for deep reinforcement learning in traffic light...
The rapid economic development has continuously improved the transportation network around the worl...
2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October...
The rapid growth of urbanization and the constant demand for mobility have put a great strain on tra...
Traffic light control (TLC) with transit signal priority (TSP) is an effective way to deal with urba...
The consequences of traffic congestion include increased travel time, fuel consumption, and the numb...
Treball fi de màster de: Master's Degree in Data Science. Curs 2018-2019Directors: Hrvoje Stojic, An...
The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future ...
Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in ...
Due to the increasing amounts of traffic in and around urban areas there is a growing need for intel...
The aim of this thesis was to explore the possibilities of using deep reinforcement learning in traf...
The current traffic light controls are ineffective and causes a handful of problems such as congesti...
Recent advances in combining deep neural network architectures with reinforcement learning (RL) tech...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
Traffic light control is one of the main means of controlling road traffic. Improving traffic contro...
Identifying the most efficient exploration approach for deep reinforcement learning in traffic light...
The rapid economic development has continuously improved the transportation network around the worl...
2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October...
The rapid growth of urbanization and the constant demand for mobility have put a great strain on tra...
Traffic light control (TLC) with transit signal priority (TSP) is an effective way to deal with urba...
The consequences of traffic congestion include increased travel time, fuel consumption, and the numb...
Treball fi de màster de: Master's Degree in Data Science. Curs 2018-2019Directors: Hrvoje Stojic, An...
The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future ...
Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in ...
Due to the increasing amounts of traffic in and around urban areas there is a growing need for intel...