Dynamic Network Capacity Allocation Using Model Predictive Control With Sparse Identification of Nonlinear Dynamics

Demand variations throughout the day and area popularity differences across the city result in spatiotemporal changes in traffic flow. One of the well-known phenomena arising from these changes is tidal traffic, characterized by an imbalance between inbound and outbound traffic on a given road. It reflects the fluctuation in the alignment between transportation system supply and demand. Lane reversal control has been a common supply-side measure for dealing with this urban traffic “sickness” by adapting road capacity allocation to the demand imbalance between two directions of a road. This study investigates the dynamic network capacity allocation control problem in the era of connected and autonomous vehicles (CAVs), which integrates dynamic traffic signal splits and lane reversal controls. Considering the high dimensionality and non-linearity of urban transportation systems, we apply the sparse identification of nonlinear dynamics (SINDy) technique to construct a sparse yet sufficiently accurate surrogate model. This model estimates the forthcoming network traffic state based on the current state and implemented control decisions. The surrogate model is integrated into a model predictive control (MPC) method, forming a SINDy-MPC framework to assist in optimal decision-making in real time. The experiments show that the system identified by SINDy exhibits stability in the presence of Gaussian noise disturbances. The proposed dynamic network allocation control scheme can effectively reduce traffic imbalance, improve traffic efficiency, and enhance traffic resilience against cyberattacks.