Over the past few decades, the intelligent transportation system (ITS) have emerged with
new technologies and becomes the data-driven ITS, because the substantial amount of data …

This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …

Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …

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 …

Predicting large-scale transportation network traffic has become an important and
challenging topic in recent decades. Inspired by the domain knowledge of motion prediction …

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 …

Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …

This paper proposes a new method in construction fuzzy neural network to forecast travel
speed for multi-step ahead based on 2-min travel speed data collected from three remote …

For the last two decades, intelligent transportation systems (ITS) have emerged as an
efficient way of improving the performance of transportation systems, enhancing travel …

Traffic flow prediction with high accuracy is definitely considered as one of most important
parts in the Intelligent Transportation Systems. As interfering by some external factors, the …