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A survey on urban traffic control under mixed traffic environment with connected automated vehicles
Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve
traffic safety. With the development of communication and automation technologies, regular …
traffic safety. With the development of communication and automation technologies, regular …
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 …
Signal timing at an isolated intersection under mixed traffic environment with self‐organizing connected and automated vehicles
This study provides a signal timing model for isolated intersections under the mixed traffic
environment consisting of connected and human‐driven vehicles (CHVs) and connected …
environment consisting of connected and human‐driven vehicles (CHVs) and connected …
A reinforcement learning approach for reducing traffic congestion using deep Q learning
Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown
dramatically, while road and transportation infrastructure capacities have yet to expand …
dramatically, while road and transportation infrastructure capacities have yet to expand …
Multi-agent simulation of autonomous industrial vehicle fleets: Towards dynamic task allocation in V2X cooperation mode
The smart factory leads to a strong digitalization of industrial processes and continuous
communication between the systems integrated into the production, storage, and supply …
communication between the systems integrated into the production, storage, and supply …
A cooperative perception based adaptive signal control under early deployment of connected and automated vehicles
Connected vehicle-based adaptive traffic signal control requires certain market penetration
rates (MPRs) to be effective, usually exceeding 10%. Cooperative perception based on …
rates (MPRs) to be effective, usually exceeding 10%. Cooperative perception based on …
Deep reinforcement learning‐based active mass driver decoupled control framework considering control–structure interaction effects
Control–structure interaction (CSI) plays a significant role in active control systems. Popular
methods incorporate actuator dynamics into an integrated control system to account for CSI …
methods incorporate actuator dynamics into an integrated control system to account for CSI …
Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization
Centralized traffic signal control has long been a challenging, high‐dimensional
optimization problem. This study establishes a simulation‐based optimization framework …
optimization problem. This study establishes a simulation‐based optimization framework …
A coordinated ramp metering framework based on heterogeneous causal inference
The coordinated ramp metering strategy aims to enhance the traffic flow on freeways by
integrating the effects of multiple on‐ramps. The success of this strategy depends heavily on …
integrating the effects of multiple on‐ramps. The success of this strategy depends heavily on …
[HTML][HTML] Improved deep reinforcement learning for intelligent traffic signal control using ECA_LSTM network
W Zai, D Yang - Sustainability, 2023 - mdpi.com
Reinforcement learning is one of the most widely used methods for traffic signal control, but
the method experiences issues with state information explosion, inadequate adaptability to …
the method experiences issues with state information explosion, inadequate adaptability to …