This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …

Short-term traffic forecasting based on deep learning methods, especially long short-term
memory (LSTM) neural networks, has received much attention in recent years. However, the …

A short-term traffic flow prediction framework is proposed for urban road networks based on
data-driven methods that mainly include two modules. The first module contains a set of …

The Intelligent Transportation System (ITS) is one of the key element to build smart cities. For
the ITS traffic flow prediction plays a major role for a better traffic monitoring and control …

The application of deep learning techniques in several forecasting problems has been
increased the last years, in many scientific fields. In this research, a deep learning structure …

Traffic flow forecasting is an acknowledged time series problem whose solutions have been
essentially grounded on statistical-based models. Recent times came, however, with …

In order to eliminate outliers in traffic flow data collection and promote the generalization
performance of traffic flow prediction, this paper proposes a dynamic optimization long short …

Traffic prediction plays an important role in evaluating the performance of
telecommunication networks and attracts intense research interests. A significant number of …

This paper presents the development and evaluation of short‐term traffic prediction models
using unidirectional and bidirectional deep learning long short‐term memory (LSTM) neural …

Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to
provide proactive traffic state information to road network operators. A variety of methods to …