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 …

To improve the accuracy of short term traffic flow prediction and to solve the problems of
nonlinearity of short term traffic flow, more noise in the data, and more difficult to determine …

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 …

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip
planning and rerouting decisions in transportation systems. Over the decades, there have …

Instability is a common problem in deep belief network–back propagation forecasting model,
and the trend of traffic data will affect the forecasting results of the model. Therefore, this …

The sustainable growth of cities created the need for better informed decisions based on
information and communication technologies to sense the city and quantify its pulse. An …

Short-term traffic flow forecasting, an important component of intelligent transportation
systems (ITS), is a challenging research direction as forecasting itself is affected by a series …

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

The development of the Internet of Things (IoT) has produced new innovative solutions, such
as smart cities, which enable humans to have a more efficient, convenient and smarter way …

Traffic flow prediction is a key step to the efficient operation in the intelligent transportation
systems. This paper proposes a hybrid method combing clustering methods and …