In recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …

Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities
of roads and transportation systems have not developed in an equivalent way to efficiently …

Vehicular traffic is endlessly increasing everywhere in the world and can cause terrible traffic
congestion at intersections. Most of the traffic lights today feature a fixed green light …

Smart cities provide new applications based on Internet of Things (IoT) technology.
Moreover, Software Defined Networks (SDNs) offer the possibility of controlling the network …

This work proposes a traffic-light scheduling framework using the deep reinforcement
learning technique to balance the traffic flow and to prevent congestion in the dense regions …

In the last few years, numerous research efforts have been conducted to merge the Internet
of Things (IoT) with smart city environments. The goal to make a city “smart” is arising as a …

In this study, we focus on optimizing traffic flow at multiple intersections. Particularly, with the
development of Internet of Things, intersection controllers are regarded as smart agents …

Intelligent traffic signal control helps to reduce traffic congestion and thus has been studied
for a few decades. Multi-intersection cooperative traffic signal control (CTSC), which is more …

The rapid increase in vehicle population has led to an increase in road congestion, pollution
and accidents. We propose a framework that optimizes collection, classification, scheduling …

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the
researchers to use these emerging mobile software packets in different simulated …