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 …

Short‐term traffic flow forecasting is a fundamental and challenging task since it is required
for the successful deployment of intelligent transportation systems and the traffic flow is …

Given the importance of continuous traffic flow forecasting in most of Intelligent
Transportation Systems (ITS) applications, where every new traffic data become available in …

Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …

Traffic state estimation is a challenging problem for the transportation community due to the
limited deployment of sensing infrastructure. However, recent trends in the mobile phone …

Accurate and timely short-term traffic flow forecasting plays a key role in intelligent
transportation systems, especially for prospective traffic control. For the past decade, a …

The realization of road traffic prediction not only provides real-time and effective information
for travelers, but also helps them select the optimal route to reduce travel time. Road traffic …

We develop a Kalman filter for predicting traffic flow at urban arterials based on data
obtained from connected vehicles. The proposed algorithm is computationally efficient and …

Current or historic traffic states are essential input to advanced traveler information, dynamic
traffic management, and model predictive control systems. As traffic states are usually not …

This paper proposes a Kalman Filter (KF) technique to predict the short-term flow at urban
arterials based on the information of connected and Bluetooth equipped vehicles. Online …