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 recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …

Traffic problems continue to deteriorate because of increasing population in urban areas
that rely on many modes of transportation, the transportation infrastructure has achieved …

The k-nearest neighbor (KNN) model is an effective statistical model applied in short-term
traffic forecasting that can provide reliable data to guide travelers. This study proposes an …

Transportation is the key to a city's prosperity, however, there is possibility that the
development and expansion of city make the transportation system complicated, uncertain …

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-
scale urban areas. Recently, some estimation and prediction methods have been proposed …

Predicting traffic flow is one of the fundamental needs to comfortable travel, but this task is
challenging in vehicular cyber–physical systems because of ever-increasing uncertain traffic …

The need for effective freight and human transportation systems has consistently increased
during the last decades, mainly due to factors such as globalization, e-commerce activities …

Deep learning has achieved good performance in short-term traffic forecasting recently.
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …

Fuzzy rule-based systems (FRBSs) is a rule-based system which uses linguistic fuzzy
variables as antecedents and consequent to represent human-understandable knowledge …