Filippos N. Tzortzoglou

Filippos N. Tzortzoglou, Logan E. Beaver, Andreas A. Malikopoulos

We present a framework for predicting human driving behavior in mixed traffic where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs), and validate it using an open-source virtual reality (VR) testbed. We estimate the time-shift parameter of Newell's car-following model for individual drivers using Bayesian linear regression and derive analytical expressions for the mean and variance of predicted trajectories. These predictions are integrated into an optimal control framework for CAV trajectory planning. To address the scarcity of mixed-traffic data, we develop a VR platform supporting realistic, multi-user driving scenarios and provide a reproducible experimental framework with a dedicated tutorial website requiring only MATLAB and Unreal Engine. Results show our approach enables efficient HDV predictions, while the VR platform offers an accessible environment for studying human behavior in mixed traffic.

Filippos N. Tzortzoglou, Pengbo Zhu, Andreas A. Malikopoulos

The rapid development of cyber-physical systems is driving a transition toward mixed traffic environments comprising both human-driven and connected and automated vehicles (CAVs). This shift presents a unique opportunity to leverage the efficient operation of CAVs to improve overall network throughput. This paper introduces a hierarchical framework designed to bridge macroscopic routing optimization at the network level with microscopic vicinity control at signalized intersections. The upper layer utilizes aggregated traffic information to provide proactive routing guidance for CAVs, aiming to minimize total travel time. The lower layer leverages local vehicle states to jointly optimize traffic light phases and individual CAV trajectories, aiming to reduce intersection crossing delays and optimize energy consumption, respectively. The effectiveness of the proposed framework is validated through SUMO on the Sioux Falls benchmark network. Results demonstrate that the integration of these macroscopic and microscopic layers yields significantly better performance compared to applying either layer in isolation, significantly improving network throughput and reducing congestion.