The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …

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 …

Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …

In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …

Big data is becoming a research focus in intelligent transportation systems (ITS), which can
be seen in many projects around the world. Intelligent transportation systems will produce a …

Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …

This study aims to combine the modeling skills of deep learning and the domain knowledge
in transportation into prediction of metro passenger flow. We present an end-to-end deep …

Due to its paramount relevance in transport planning and logistics, road traffic forecasting
has been a subject of active research within the engineering community for more than 40 …

We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

Neural networks have been extensively applied to short-term traffic prediction in the past
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …