The role of predictive effect in lattice model incorporating traffic jerk effect

Abstract

In traffic networks, the intricate traffic congestion is the result of the abrupt deceleration and acceleration of non-motor vehicles that react by observing downstream situations. Furthermore, as information technology (IT) continues to grow and evolve, drivers can now obtain a precise assessment of the present status of real-time traffic on a prior basis. The traffic congestion that occurs due to traffic jerks may be minimized with the use of prior information (known as the predictive effect). To assess the impact of the predictive effect and traffic jerk effect on homogeneous vehicular flow, an extended lattice hydrodynamic model is proposed. Linear and nonlinear stability analysis is used to investigate the proposed model theoretically. The approach of reductive perturbation is used to derive the modified Korteweg–de Vries (mKdV) equation. Density waves in the structure of kink–antikink soliton waves around the critical point are formed. Further, numerical simulations are carried out to validate the theoretical predictions, confirming that incorporating the predictive effect into a traffic system may decrease traffic congestion more efficiently.