Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are
expected to revolutionize urban mobility. Despite the expected benefits, this emerging …

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could
help reduce traffic jams. Deep reinforcement learning methods demonstrate good …

Deep learning methods are in the forefront of techniques used to perform complex controls
in autonomous vehicles (AVs). Such methods are vulnerable to nuanced types of …

The analogy between the theory of phase transitions in simple fluids and vehicular traffic
flow has long been suspected, promising a new level of understanding of urban congestion …

To mitigate the traffic oscillations generated when traffic flow encounters a red light at a
signalized intersection, many studies have used reinforcement learning to control connected …

In one-dimensional heterogeneous models of hard-particle flux (like vehicular traffic), the
system dynamics depend strongly on the behavior of the leading particle. In models …

Abstract System engineering is used to analyze traffic delay on a test track as a dynamic
process using microscale time data. Each driver is required to start from rest and stop at a …

Kinetics of dilute heterogeneous traffic on a two-lane road is formulated in the framework of
Ben-Naim–Krapivsky model and stationary state properties are analytically derived in the …

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could
help reduce traffic jams. Deep learning methods are in the forefront of techniques used to …

In one-dimensional, heterogeneous systems, the whole traffic dynamics depend strongly on
the behavior of the leading vehicle. This result holds for a class of vehicular traffic models …