Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …

Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning.
Most existing traffic flow models fail to make full use of the temporal and spatial features of …

Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been
the focus of many studies in the last few decades. Existing traffic flow prediction models …

Tree based ensemble methods have reached a celebrity status in prediction field. By
combining simple regression trees with 'poor'performance, they usually produce high …

This paper proposes a new method in construction fuzzy neural network to forecast travel
speed for multi-step ahead based on 2-min travel speed data collected from three remote …

Transportation systems might be heavily affected by factors such as accidents and weather.
Specifically, inclement weather conditions may have a drastic impact on travel time and …

The use of statistical models for analyzing traffic safety (crash) data has been well-
established. However, time series techniques have traditionally been underrepresented in …

Accurate and robust short-term traffic prediction is an important part of advanced traveler
information systems. With the development of intelligent navigation and autonomous driving …

In Internet of Vehicles (IoV), accurate traffic flow prediction is helpful for analyzing road
condition and then timely feedback traffic information to managers as well as travelers …