In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …

Traffic control for large-scale urban road networks remains a challenging problem.
Aggregated dynamical models of city-scale traffic, based on the macroscopic fundamental …

Data-driven control without using mathematical models is a promising research direction for
urban traffic control due to the massive amounts of traffic data generated every day. This …

City-wide control and coordination of traffic flow can improve efficiency, fuel consumption,
and safety. We consider the problem of controlling traffic lights under fixed and adaptive …

In recent years, several perimeter control strategies have been proposed for traffic
management in cities. The common factor found in these works is the use of Macroscopic …

In urban traffic networks, abundant traffic data is generated everyday. Therefore, data-driven
control approaches for urban traffic control without utilizing mathematical models might be a …

This paper studies the edge controllability of signed networks, where the dynamics taking
place on edges and the interaction type over each edge can be cooperative or antagonistic …

We consider the traffic light control problem for a single intersection modeled as a stochastic
hybrid system. We study a quasi-dynamic policy based on partial state information defined …

This article proposes a traffic control scheme to alleviate traffic congestion in a network of
interconnected signaled lanes/roads. The proposed scheme is emergency vehicle-centered …

In this paper, we design a stochastic model predictive control (MPC)-based traffic signal
control method for urban networks when the uncertainties of the traffic model parameters …