The primary goal with which any transportation system is designed is to make efficient use of
the available infrastructure to achieve better level of service (LoS). However, LoS is …

This paper presents a novel model for estimating the number of vehicles along signalized
approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to …

This paper presents a novel method for estimating the number of vehicles traveling along
signalized approaches using probe vehicle data only. The proposed method uses the …

The paper presents a nonlinear filtering approach to estimate the traffic stream density on
signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle …

Jam density, or its reciprocal jam spacing, is a parameter difficult to estimate. In fact, most
traffic signal control and traffic state estimation studies published until date generally …

This paper introduces novel approaches for the estimation of the traffic stream density. First,
an artificial neural network (ANN) data-driven approach is developed to estimate the level of …

This paper introduces a two-stage adaptive Kalman filter algorithm to estimate and predict
traffic states required for real-time traffic signal control. Leveraging probe vehicle trajectory …

This paper presents a bi-level Kalman filter methodology for real-time traffic state estimation
and short-term prediction at signalized intersections. At the upper level, turning movements …

With the deployment of partially and highly automated vehicles, the automotive industry is
greatly increasing its influence on road traffic. In order to ensure a positive influence of …

Connected vehicles (CVs) have the potential to collect and share information that, if
appropriately processed, can be employed for advanced traffic control strategies, rendering …