Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to
understand transportation traffic patterns and their impact on wireless network designs and …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

With the recent advances in deep learning, data-driven methods have shown compelling
performance in various application domains enabling the Smart Cities paradigm …

The significant increase in world population and urbanisation has brought several important
challenges, in particular regarding the sustainability, maintenance and planning of urban …

This study proposes a data fusion and deep learning (DL) framework that learns high-level
traffic features from network-level images to predict large-scale, multi-route, speed and …

Accurate traffic state prediction is the foundation of transportation control and guidance. It is
very challenging due to the complex spatiotemporal dependencies in traffic data. Existing …

Traffic forecasting is a crucial aspect of Intelligent Transportation Systems, as it has the
potential to improve the mobility and efficiency of transportation in cities while reducing costs …

Abstract# The significant increase in world population and urbanisation has brought several
important challenges, in particular regarding the sustainability, maintenance and planning of …