The rise of smart cities has necessitated the development of advanced traffic management
systems that can adapt to dynamic urban traffic conditions. Traditional traffic signal control …

The paper uses Deep Reinforcement Learning (DRL) to traffic signal regulation, solving
urban traffic congestion. Our research paper demonstrates the simulation of intricate traffic …

One of the focal points in the field of intelligent transportation is the intelligent control of traffic
signals (TS), aimed at enhancing the efficiency of urban road networks through specific …

Adaptive traffic signal control techniques have been developed in numerous studies to
increase traffic flow efficiency. Using traffic signals to design an adaptive traffic management …

In this work, we study adaptive data-guided traffic planning and control using Reinforcement
Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep …

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving
traffic flow and reducing congestion, which is a pervasive issue impacting urban areas …

Intelligent traffic signal control plays a crucial role in alleviating traffic congestion. With
increasingly available traffic data, there is a trend to use deep reinforcement learning (DRL) …

A traffic signal is a fundamental part of the traffic control system to reduce congestion and
enhance safety. Since the inception of motorized vehicles, traffic signal controllers are put in …

The development of Intelligent Traffic Signal Control (ITSC) systems is crucial for enhancing
traffic flow and mitigating congestion, which is a widespread problem in urban areas …

Traffic Signal Control using Reinforcement Learning has been proved to have potential in
alleviating traffic congestion in urban areas. Although research has been conducted in this …