In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

Urban transportation system is a large complex nonlinear system. It consists of surface-way
networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and …

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …

Traffic signal control is an important and challenging real-world problem, which aims to
minimize the travel time of vehicles by coordinating their movements at the road …

Exploiting reinforcement learning (RL) for traffic congestion reduction is a frontier topic in
intelligent transportation research. The difficulty in this problem stems from the inability of the …

With the booming development of Internet-of-Things (IoT) and communication technologies
such as 5G, our future world is envisioned as an interconnected entity where billions of …

This paper presents an intelligent traffic control system to pass emergency vehicles
smoothly. Each individual vehicle is equipped with special radio frequency identification …

We propose, for the first time, a reinforcement learning (RL) algorithm with function
approximation for traffic signal control. Our algorithm incorporates state-action features and …

Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free
reinforcement learning (RL) approach is a powerful framework for learning a responsive …

In this paper, we focus on computing a consistent traffic signal configuration at each junction
that optimizes multiple performance indices, ie, multi-objective traffic signal control. The multi …