This study presents a data-driven framework for modeling complex systems, with a specific
emphasis on traffic modeling. Traditional methods in traffic modeling often rely on …

As with other complex dynamic systems, modeling traffic systems requires an accurate
understanding of how the individual components are coupled, which can be challenging …

Understanding complex traffic dynamics, characterized by the interplay between vehicle
automation technologies, human drivers, and diverse transportation modes, presents …

Modeling stochastic traffic behaviors at the microscopic level, such as car-following and lane-
changing, is a crucial task to understand the interactions between individual vehicles in …

Analyzing the impacts of model parameters on model outputs is an important but
challenging topic for scientific research involving simulation models. Global Sensitivity …

We propose a physics informed statistical framework for traffic travel time prediction. On one
side, the discrepancy of the considered mathematical model is represented by a Gaussian …

The emergence of intelligent connected vehicles (ICVs) is expected to contribute to
resolving traffic congestion and safety problems; however, it is inevitable that ICV safety …

With the advent of connected and automated vehicles, naturalistic traffic modeling is
becoming increasingly important for the purposes of congestion control via cooperative …

Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple
surrounding vehicles. The correlations between road users vary over time, the degree of …

We develop theoretical and computational tools which can appraise traffic flow models and
optimize their performance against current time-series traffic data and prevailing conditions …