An Extended Multilane Lattice Hydrodynamic Model Considering the Predictive Effect of Drivers under Connected Vehicle Environment

Abstract

As the main road pattern, multilane roads are prevalent on high-grade highways. In a connected vehicle environment, drivers can perceive the full view of traffic information on each lane, which provides more opportunities for flexibly changing lanes on multilane highways. In addition, drivers can predict the traffic status on short notice and regulate the vehicle’s operating state in advance. This study determined the predictive effect of drivers in a multilane scenario using the lattice hydrodynamic model. The stability criteria of the proposed model were deduced via the reductive perturbation method; when the stability conditions do not hold, the modified Korteweg–de Vries (mKdV) equation can be deduced. By solving this above equation, we derived the kink–antikink soliton wave solution, which can be used to analyze and explain the formation and evolution process of traffic jams. The results show that the number of lanes and the prediction time of drivers considerably affect the stability of traffic flow. Simulation examples verified that when the predictive time is fixed and the number of lanes increases from 1 to 4, the fluctuation amplitude of traffic density decreases from 0.2 to 0.08 even with exogenous initial disturbance; when the number of lanes is fixed, the density fluctuation amplitude decreases as the predictive time increases; and when the predictive time increases to 0.7, the fluctuation amplitude of traffic density approximates to 0 and is a uniform flow.