Traffic estimation using probe vehicle data is a crucial aspect of traffic management as it
provides real-time information about traffic conditions. This study introduced a novel …

Traffic density estimation using various Market Penetration Rates (MPRs) of Connected
Vehicle (CV) data represents an area in need of continued research and refinement to fully …

Connected automated vehicles (CAVs) hold promise to replace current traffic detection
systems in the near future. However, traffic state estimation, particularly flow rate, poses a …

Traffic Jam is one of the major problems of modern urban life. Regardless of the socio-
economic structure of a country, almost all the countries of the world suffer from this problem …

Traffic prediction is a major component of any traffic management system. With the increase
in data sources and advancement in connectivity, data analysis and machine learning …

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 …

Traffic estimation is imperative for conducting fundamental transportation engineering tasks
such as transportation planning and traffic safety studies. Additionally, traffic prediction is …

Determining the traffic state is one of the most attractive problems for experts in the field of
Intelligent Transport Systems (ITS). In this paper, a deep learning model for determining the …

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including
trajectories and driving events (eg, hard braking) collected by vehicle manufacturers when …

Network-wide traffic prediction at the level of an intersection can benefit transportation
systems management and operations. However, traditional traffic modeling approaches …