Hybrid Neural Network Learning for Multiple Intersections along Signalized Arterials: A Microscopic Simulation vs. Real System Effect
Hong Wang · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2023
Control of traffic flow along arterials requires signal timing control at intersections so that the resulting traffic flows along the arterials are as smooth as possible with minimized energy usage. With advances in sensing technologies, various data sets are available, allowing effective data-driven modeling to be conducted for further controller design that produces better signal timing control at intersections. In this paper, which is an extended version of our conference paper, a hybrid neural network (HNN) is proposed to model the multiple intersections along a signalized arterial in Honolulu, for which the modeling structure and relevant training algorithms have been developed. The proposed HNN consists of linear dynamics and a nonlinear function; the linear dynamics present a simplified opportunity for the closed-loop control design, and the nonlinear function is a representation of unmodeled dynamics as a function of previously available system inputs and outputs. The modeling and training are performed simultaneously for linear dynamics matrices and the weights of neural networks that approximate the nonlinear dynamics of the system. A preliminarily calibrated VISSIM microscopic traffic simulation platform is proposed to learn the real system using HNN modeling in which data collected from VISSIM simulations are used to estimate the system’s unknown features. The modeling results using real data and VISSIM-generated data are compared, and the desired modeling results are obtained.