The prediction of high-resolution hourly traffic volumes of a given roadway is essential for
transportation planning. Traditionally, automatic traffic recorders (ATR) are used to collect …

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 …

Traffic forecasting remains an active area of research in the transport and data science
fields. Decision-makers rely on traffic forecasting models for both policy-making and …

Short-term traffic flow prediction plays a critical role in Intelligent Transportation System
(ITS), and has attracted continuous attention. Previous studies have focused on improving …

Short-term traffic forecasting capabilities on freeways and major arterials have received
special attention in the past decade primarily because of their vital role in supporting various …

The accurate prediction of real-time traffic flow is indispensable to intelligent transport
systems. However, the short-term prediction remains a thorny issue, due to the complexity …

Traffic flow prediction plays an important role in intelligent transportation systems. To
accurately capture the complex non-linear temporal characteristics of traffic flow, this paper …

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 …

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

With the increasing demand for intelligent transportation systems, short-term traffic flow
prediction has become an important research direction. The memory unit of a Long Short …