In real-time traffic management and intelligent transportation systems (ITS) applications, an
accurate picture of the prevailing traffic state in terms of speeds and densities is critical, for …

This paper provides a two-step approach based on the stochastic differential equations
(SDEs) to improve short-term prediction. In the first step of this framework, a Hull-White (HW) …

This paper presents a methodology for automatic measurement of major traffic variables for
Intelligent Transportation Systems applications (ITS) in real-time. It is an illustration of the …

The prediction of short-term traffic flow is critical for improving service levels for drivers and
passengers as well as enhancing the efficiency of traffic management in the urban …

In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …

Some researchers treat traffic flow as an entirety while predicting short-term traffic flow.
Through analyzing real-world traffic flow, we have found that urban traffic shows a stable …

Traffic management and traffic information are essential in urban areas and require reliable
knowledge about the current and future traffic state. Parametric and nonparametric traffic …

Obtaining accurate information about current and near-term future traffic flows of all links in a
traffic network has a wide range of applications, including traffic forecasting, vehicle …

This study presents a data segmentation method, which was intended to improve the
performance of the k‐nearest neighbours algorithm for making short‐term traffic volume …

This paper formulates the traffic flow forecasting task by introducing a maximum correntropy
deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error …