  Traffic flow prediction is an important research issue for solving the
traffic congestion problem in an Intelligent Transportation System (ITS).
Traffic congestion is one of the most serious problems in a city, which can be
predicted in advance by analyzing traffic flow patterns. Such prediction is
possible by analyzing the real-time transportation data from correlative roads
and vehicles. This article first gives a brief introduction to the
transportation data, and surveys the state-of-the-art prediction methods. Then,
we verify whether or not the prediction performance is able to be improved by
fitting actual data to optimize the parameters of the prediction model which is
used to predict the traffic flow. Such verification is conducted by comparing
the optimized time series prediction model with the normal time series
prediction model. This means that in the era of big data, accurate use of the
data becomes the focus of studying the traffic flow prediction to solve the
congestion problem. Finally, experimental results of a case study are provided
to verify the existence of such performance improvement, while the research
challenges of this data-analytics-based prediction are presented and discussed.
