Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

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 …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …

The rapid development of intelligent and connected technologies is conducive to the
efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy …

Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …

In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …

The technological landscape of intelligent transport systems (ITS) has been radically
transformed by the emergence of the big data streams generated by the Internet of Things …