Designing Hierarchical Infrastructure-based Traffic Control Frameworks for Large-Scale Heterogeneous Traffic Networks

Autonomous vehicles have gained huge interest across private industry, academia, government, and the public because they promise higher road efficiency, improved safety, better energy consumption, and improved emissions. However, the widespread adoption of autonomous vehicle technology will likely take place over several years (if not decades) as the technology becomes more widely accepted by the general public and more cost-effective. Therefore, there will be a long period of time when we have both autonomous and human-driven vehicles sharing the same road and it is essential to develop traffic management strategies that take the uncertainty associated with the heterogeneity in the traffic networks into account. Furthermore, it is crucial to understand the extent to which these control strategies improve the performance of the traffic network.
This research aims to develop, enhance, and validate hierarchical infrastructure-based control framework designs for improving the mobility of large-scale heterogeneous traffic networks. In this work, heterogeneity is defined as a multi-vehicle traffic network consisting of Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs), distinguished by their operational characteristics and controllability. To capture the realistic nature of large-scale heterogeneous traffic networks, we adopt the heterogeneous (multi-class) METANET model wherein the density and velocity dynamics of each vehicle class in each cell are described mathematically.
Moreover, in this research, we propose a hierarchical distributed infrastructure-based control framework to manage large-scale heterogeneous traffic networks. At the lower-level, we employed the Distributed Filtered Feedback Linearization (D-FFL) controller which only requires limited information from the plant model. The purpose of this controller is to track the desired density of each vehicle class in the target cells which is set by the upper-level controller. D-FFL tracks the reference density by controlling the suggested velocity of vehicles in the target cell and its upstream cell. At the upper-level, in our initial design, a Distributed Extremum-Seeking (D-ES) controller is designed and implemented to find the optimal operating densities of each vehicle class in the target cells over time. Gradient-based D-ES is a model-free, real-time adaptive control algorithm that is useful for adapting control parameters to unknown system dynamics and unknown mappings from control parameters to an objective function. To improve the performance of the designed hierarchical controller and reduce the convergence time, we designed and implemented Lyapunov-based Switch Newton Extremum Seeking (LSNES) at the upper level of the hierarchy to feed the optimal density of each vehicle class in the target cells to the lower-level controller. One of the key distinctions between the Newton algorithm and the gradient algorithm is that the convergence of the former is not solely contingent on the second derivative (Hessian) of the cost map and it is user-assignable.
Finally, we established a MATLAB-VISSIM COM interface that allows closed-loop control of a simulated traffic scenario in PTV-VISSIM to test and validate the effectiveness of the distributed control approaches in large-scale traffic networks. The simulation results show that our control framework design can effectively reduce congestion and prevent congestion back-propagation during peak hours in large-scale traffic networks.