[HTML][HTML] Artificial intelligence-based adaptive traffic signal control system: A comprehensive review
The exponential increase in vehicles, quick urbanization, and rising demand for
transportation are straining the world's road infrastructure today. To have a sustainable …
transportation are straining the world's road infrastructure today. To have a sustainable …
A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles
Recent advancements in connected automated vehicles (CAVs) and reinforcement learning
(RL) hold significant promise for enhancing intelligent traffic control systems. This paper …
(RL) hold significant promise for enhancing intelligent traffic control systems. This paper …
A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions
R Zhao, Y Li, Y Fan, F Gao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely
without human intervention. AD agents generate driving policies based on online perception …
without human intervention. AD agents generate driving policies based on online perception …
A digital twin-based traffic light management system using BIRCH algorithm
Urban transportation networks are vital for the economic and environmental well-being of
cities and they are faced with the integration of Human-Driven Vehicles (HVs) and …
cities and they are faced with the integration of Human-Driven Vehicles (HVs) and …
Analysis of the impact of heterogeneous platoon for mixed traffic flow: control strategy, fuel consumption and emissions
Compared with traditional vehicle longitudinal spacing control strategies, the combination
spacing strategy can integrate the advantages of different spacing control strategies …
spacing strategy can integrate the advantages of different spacing control strategies …
Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections
Cooperative control of intersection signals and connected automated vehicles (CAVs)
possess the potential for safety enhancement and congestion alleviation, facilitating the …
possess the potential for safety enhancement and congestion alleviation, facilitating the …
Toward Automatic Market Making: An Imitative Reinforcement Learning Approach With Predictive Representation Learning
Market making (MM) is a crucial trading problem, where a market maker stands ready to buy
and sell the asset at a publicly quoted price to provide market liquidity continuously. The …
and sell the asset at a publicly quoted price to provide market liquidity continuously. The …
Particle-Assisted Deep Reinforcement Learning for Quantum State Manipulation
H Yu, X Liu, B Wang, X Zhao - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Applying deep reinforcement learning (DRL) to solve quantum control problems has
become a popular research direction. However, the exploration capability and reward …
become a popular research direction. However, the exploration capability and reward …
Estimation of vehicle control delay using artificial intelligence techniques for heterogeneous traffic conditions
The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM)
used for the delay estimation revolve around the mathematical hypothesis and assumptions …
used for the delay estimation revolve around the mathematical hypothesis and assumptions …
A hierarchical intersection system control framework in mixed traffic conditions
C Liu, H Jia, Q Huang, Y Cui - Expert Systems with Applications, 2025 - Elsevier
Signalized intersections play a crucial role in urban road system and are a significant source
of vehicle delays. Conventional intersection control methods struggle to cope with …
of vehicle delays. Conventional intersection control methods struggle to cope with …