Mesoscopic Traffic Flow Model Considering Overtaking Requirements

Abstract

Overtaking probability formula in the Prigogine—Herman traffic flow mesoscopic model is the function of traffic density, which is a linear function and does not consider overtaking requirements. In this paper, we use desired speed to improve the overtaking probability formula and propose a new overtaking probability formula that is nonlinear and a corresponding traffic flow mesoscopic model. The improved model can simultaneously consider traffic density and overtaking requirements, thus reflecting traffic flow operation realistically. We use the proposed model to simulate the diffusion process of vehicles from a high-density section to a low-density section. The example analyzes the difference between the linear overtaking probability formula and the parabolic Greenshield’s overtaking probability formula. Results show that the linear overtaking probability formula evolves quickly and converges to three speed classes. The parabolic Greenshield’s overtaking probability formula converges to six speed classes and can reflect speed distribution evolution reasonably.