Exploring visibility effects with flux variations under autonomous and human-driven vehicles environment in heterogeneous lattice model

This paper presents a novel lattice hydrodynamic model that integrates the flux difference with the visibility effect in a mixed environment of autonomous vehicles (AVs) and human-driven vehicles (HVs). The flux difference term accounts for disparities in vehicle flow between AVs and HVs, as well as a visibility factor to reflect the influence of reduced driver perception on human-driven vehicles. Mainly, the model is designed to capture the unique characteristics of both vehicle types and their interactions, focusing on how limited visibility and varying flux between vehicle groups impact traffic stability and congestion dynamics. Numerical simulation is conducted to evaluate the model’s performance, highlighting the effects of these factors on traffic flow stability, phase transitions and jam formation. We analyze spectral entropy to explore the impact of flux difference, visibility, HV-AV penetration effect and memory information on mixed traffic dynamics from a deeper perspective through simulation. The results indicate that the influence of flux difference and visibility significantly improves traffic stability, particularly under adverse visibility conditions. The enhanced visibility and reaction time parameters of HVs vehicles may complicate in-vehicle interactions as compared to AVs, potentially resulting in delayed responses and increased susceptibility to disturbances. This work provides insights into the role of mixed vehicle environments and offers potential strategies for optimizing traffic flow in future smart transportation systems.